The eLife experiment is welcome, but …….

The journal eLife has declared its new peer review policy which is a bold experiment in science communication. In a recent editorial ( they declare that eLife will not use peer reviews for a dichotomous decision of accept-reject. Instead they will publish every paper that they choose to review along with the reviewers’ comments. Further they say that the authors will have sufficient freedom to use this peer review to publish elsewhere etc.

To some extent, this is precisely what I had been saying for quite some time (but with important differences). Of course, many others would have thought that way. As the article says, “Nobody who interacts with the current publishing system thinks it works well, and we all recognize that the way we use it impedes scientific progress”. Since there appears to be a consensus that the current system of science publishing is deeply flawed, there need to be alternative models and there have been some experiments on alternative publishing models. But one thing is lacking.

Science is a human endeavor and therefore is clearly subject to principles of human behavior. Any new system being designed needs to be based on our understanding of behavior. If it is based only on ideology, but ignores behavior, it is bound to fail in realizing its objectives. It may become stable and popular but that is not the measure of success. How far it serves the original purpose should be the measure of success. The central question is how to design a system that will serve the purpose, given the behavioral choices of all stakeholders in the field. The reason why the existing system is flawed lies in people’s behavior and a new system can also get easily corrupt for the same reason. It is therefore necessary to analyze the reasons behind the problems in the current system and see whether we have been addressing these problems in designing a new system. I have written a detailed article which is available as a preprint for the last 5 years ( As expected in the article itself, this couldn’t have been published by a traditional journal, and the prediction has turned out to be correct so far. In these 5 years my analysis of the behavior of scientific community has gone much ahead. Here I will only mention a couple of behaviorally important points relevant to the eLife experiment that has begun.

The committees that decide recruitments, promotions or funding look at where a candidate has published rather than what is published. This is not without reason. The journal names and impact factors save them the cost of reading. Reading incurs substantial cost. IFs are popular only because they save the cost of reading. There can be an inexpensive pretense of evaluation without evaluating anything. So although IFs are not scientifically sound, they are behaviorally profitable and therefore the committees will not give up on them easily. The eLife’s stand of replacing the accept reject-decision by publishing peer reviews will compel the committee members to read research, and they will be most reluctant to do so. For over 2-3 decades, committee members are addicted to the ‘evaluate without reading’ package and de-addiction is not going to be easy.

The accept-reject decision cannot be replaced as long as the prestige of journal matters. The more prestigious journals will be overburdened with submissions and they can review only a limited number. So desk rejection will become even more important and there all the biases caused by the dichotomous decision will return in perhaps a worst form. eLife itself says “We will publish every paper that we send out for review”, which means a large number will be rejected without giving reason at some one’s vim. This decision is bound to be guided by private cost benefits of the editor which is not going to eliminate the existing biases.

There is one more potential contradiction. The elites of science control most of the prestigious journals. Therefore they are not so unhappy about conventional peer review systems. Peer reviews have biases by gender, country, race, reputation etc. So the underprivileged class of science will find the open peer review system more beneficial. But mostly the underprivileged are also poorly funded. They will not be able to afford the author charges of 2000 dollars per paper. So the change may not benefit the ones who are looking for a change. The journal has a facility of waiving charges, but how efficiently it works will decide everything. It is quite likely that the profitability or even sustainability of the journal will be compromised if waivers are really given to everyone who needs. There are more problems with the suggested change. But nevertheless, any experiment is welcome. The risk in doing such experiments without sufficient thinking is that failure of such experiments will further strengthen the flawed system once again. Therefore it is necessary to design behavior based systems right away. In economics and management, designing behavior informed systems is not a new concept. There is substantial research on it. Why not utilize this in science? And if the field of science itself fails to use novel scientific concepts, who else will?

A smarter way to suppress inconvenient science

After a delay of 6 months, the journal PLOS One returned our manuscript saying that they could not find an academic editor and reviewers for our manuscript. PLOS One is a fairly open minded journal and has a team of editors representing wide diversity of fields. That’s why this kind of response is quite surprising. This is only for the second time in my life I received this response. Earlier incident was with the journal Biology Direct. Are the two incidents only a matter of rare chance? Or are there any specific reasons to it?

One thing common about both is that both were about diabetes, highlighting models that are at substantial deviation from the prevalent mainstream thinking in the field. I think there lays the reason.

What was our paper about? It pointed out a large number of anomalies in the prevalent theory of glucose dysregulation in type 2 diabetes. It listed dozens of mismatches between the theory and an array of reproducible experimental or epidemiological findings. It also suggested an alternative model that could account for almost every anomaly in a coherent thread of logic. Classically type 2 diabetes is believed to result from an elusive concept of “insulin resistance” and inadequate compensatory insulin response. We, on the other hand assumed with sufficient evidence in hand that diabetes begins with vasculopathy. Because of deficient vasculature there is inadequate and defective glucose transport to the brain which makes the brain deficient in glucose. Deprived of sufficient glucose, the brain instructs the liver to release more glucose in blood. Vasculopathy is long known to be a characteristic of diabetes but the thinking was that chronic rise in glucose is the cause of vasculopathy. We are saying the reverse, vasculopathy the cause of rise in sugar. There is clear demonstration that transport of glucose from blood to brain is reduced prior to hyperglycemia. Further, ALL the experimental and epidemiological patterns not explained by the insulin resistance theory are explained with complete coherence by the “vasculopathy first” model. Therefore the alternative model looks more promising. There also exists published evidence that early signs of vasculopathy are seen much prior to hyperglycemia.

The catch is, if we accept the alternative model, the entire line of treatment of diabetes will become completely redundant. That would lead to collapse of a trillion dollar business. But that is much ahead in the sequence. Right now we are not over-claiming. We only say in this paper that the alternative model explains almost all the anomalies and therefore needs to be considered seriously and trigger research on a new line.

How do researchers in a field react to a finding, hypothesis, model or synthesis that directly contradicts the prevalent theory? You would expect them to critically view the new finding, may be find flaws in the argument, aggressively criticize, debate and so on. I am ready to believe that a welcome response is highly unlikely. It would be natural to expect heavy criticism. This might happen if the new argument is inherently flawed and it is easy to find the flaws in it. But what if the prevalent theory itself is flawed and the new argument it substantially stronger and sound in terms of logic, mathematics and evidence?

From repeated experience I know what a typical response of scientists is, particularly from the field of biomedicine. They prefer to keep mum. They neither accept nor reject any disruptive thinking or evidence. They pretend that they just haven’t heard of it. Criticism can be replied to. A debate is likely to take a logical path so that ultimately truth will prevail with a good chance, if not every time. But the strategy that always defeats novel thinking is “silence”. When the giants in a community have vested interests in a prevalent theory and someone makes a sound case that it is wrong, they just keep mum, pretend that nobody said anything; they did not hear anyone saying anything. In the days of hierarchical structure of science publishing this strategy can perhaps never be defeated. The giants in the field can block the new thought from getting published in the flagship journals of the field. They don’t care if it gets published anywhere else because they know nobody reads research anyway. Research is propagated only through a handful of journals; that too only the through the titles and abstracts. Rarely if ever, research papers are read completely.  So often the data in the paper contradicts the statements in the abstract. But everyone reads only the abstract and therefore truth remains masked. If we point out stark difference in the data and the conclusions in a paper, the journal is guaranteed to not respond.

This is not different in principle, from the responses of researchers to a disruptive idea described by Thomas Kuhn, albeit two major differences. One is that of difference in culture of the research fields. Kuhn mostly talked about physics in which ideas are debated. Debate is not in the culture of biomedicine. They have smarter ways to suppress alternative thinking. The second difference is that Kuhn wrote when peer review was not a mandatory norm in science publishing. Now peer review is another weapon by which any upcoming thought can be swiftly killed. And you need not waste any time in reading and commenting as well. Just decline to handle the manuscript and that is enough!! Here is our manuscript in a preprint form ( and see below the correspondence with the editors.

PLOS ONEFri, Aug 12, 6:17 PM
to Milind

Dear Dr. Watve,

I am writing with the difficult news that we have not been able to secure an Academic Editor to handle your manuscript “Hyperglycemia in type 2 diabetes: physiological and clinical implications of a brain centered model” (PONE-D-22-04305). Additionally, we have been unable to secure feedback from peer reviewers. We have therefore reluctantly decided that we must return your manuscript to you without review.

I recognize that this decision will be frustrating — it is our desire to provide every suitable manuscript the opportunity for review and evaluation by experts in the research community — and I sincerely apologize that we have not been able to do so in this case. We have exhausted the pool of potential PLOS ONE Academic Editors qualified to handle your manuscript but have not been able to secure a commitment to handle the submission. We have also invited a number of peer reviewers with relevant expertise, but we have not been able to secure the reviews required to support an editorial decision. We are withdrawing your manuscript from consideration to prevent further delays in the assessment of your submission, and so that you can move forward immediately if you choose to submit your work elsewhere.

Again, I am very sorry not to have more positive news for you. I wish you the best in finding an alternative venue for this work.

Best regards,
Emily Chenette

Milind Watve <>Sun, Aug 14, 10:23 AM
to PLOS, bcc: Akanksha

Dear Emily,

I understand the agonies of editors. No issues. But I have one request. 

I would like to have your consent to post this letter in the public domain. It is very likely to be a remarkable event in the history of science and students of the history and philosophy of science need to have access to this information. How people in a field react to a paper challenging an existing dogma is a very important question in the history and philosophy of science and making this letter public is extremely essential. Therefore I want to append it to the preprint, as well as write an article about it on my blog on which I have often written about problems in science and science publishing. Link here if you want to view it (

Awaiting your response. 


(Dr. Milind Watve)

जुस्ते हक़ की रहगुजर में जो सियाही है, मेरी है

उस मजाज़े आराइश में तेरा ही बस हो तो क्या है

The darkness on the path to truth is my homeground. If the dazzling lights in the rest of the world are under your command, why should I care!!
PLOS ONE <>Sun, Aug 14, 10:24 AM
to me

Dear Milind Watve

Thank you for contacting PLOS ONE. We will reply to your query as soon as we are able.

In the meantime, please take a look at the following links for more information about our processes:

A message to our community regarding COVID-19
Submission Guidelines
Reviewer Guidelines
Publication Criteria
Editorial & Peer Review Process
PLOS ONE Video Shorts

We appreciate you reaching out and will be back in touch shortly.

All the best,


Case 07687026

Milind Watve <>Mon, Aug 29, 9:20 PM (13 hours ago)
to plosone

Dear Editor,

This is to inform you that since I did not get any reply from you for over two weeks, I am assuming that you have no objection if I publish your letter in any appropriate context, in a respectful manner. 


(Dr. Milind Watve)

जुस्ते हक़ की रहगुजर में जो सियाही है, मेरी है

उस मजाज़े आराइश में तेरा ही बस हो तो क्या है

The darkness on the path to truth is my homeground. If the dazzling lights in the rest of the world are under your command, why should I care!!

A welcome rejection:

I am happy to receive a rejection to my manuscript. I wrote a MS about the biases in peer reviews, how some basic principles of human behaviour create these biases and suggested a behaviour based system design for scientific publishing that would minimize, if not eliminate biases. Anticipating that criticising peer reviews would create controversies, I communicated this MS to the Journal of Controversial Ideas (JCI). After almost one year I received a rejection. One of the main reasons for the rejection is that this is not a controversial issue at all. “The idea that peer review is flawed and creates bad incentives is widely held by academics.”

This is unique experience. The paper is rejected because the peer reviewers agree that peer review is itself a bad idea. The paper is rejected because peer reviewers agree with one of my main arguments. They do contradict and strongly disagree with some of my other arguments (and still say that there is no controversial idea in this). I must say that this is one of the rare instances of a thorough and thoughtful peer review I received. I don’t agree with all that the reviewers say, which is fine. But I certainly have much to learn from what they say and this is not a very common experience. Out of the nearly 100 peer reviewed papers I published (which means those many acceptances along with a greater number of rejections) between 20-30 times I thought I received comments that would really improve the quality and rigor of the paper. This rejection is certainly one of them. This means that at least some times peer reviews rally help. The percentage in my experience was about 10 %.

Whether now I would communicate the paper to some other peer reviewed journal or not, I haven’t decided. But I am not too keen for obvious reasons. If everyone agrees that peer reviews are weird and flawed, why should we consider only peer reviewed publications as science? Peer reviews actually have no relevance to science. No doubt they have a relevance to making a career in science because there is a ritual of listing and counting peer reviewed papers. Every selection, appointment, promotion etc has to go through this ritual. Now I am not in the race of making a bright career. So I suffer no loss by getting my papers rejected.

But a curious observer in me is not dead. It won’t be until I remain cognitively healthy (by medical definitions). So I have a number of questions. If the flawedness of the peer review system is universally accepted and there is no controversy about it, why do we still depend so much on it? If the main pillar of science, that is publication of the outcome, is so flawed why we fail to see that it makes the entire field of science flawed? Why the attempts to change the system are so half hearted, ephemeral and almost always a failure until now? While new fields like behaviour based policy making are thriving, why don’t we apply them to science publishing? I did my own behavioural analysis of different players in scientific publishing and designed an alternative system. It is not necessary that everyone agrees with it. But doesn’t it deserve a debate? Shouldn’t my ideas be published in order to generate a debate? Why are people of science running away from addressing the fundamental flaws in the field?

Perhaps I know the answer. There is a in-power group which decides the protocols of science publishing as well as funding. The group that already enjoys the power does not suffer by the flaws. People who actually suffer by the unfair systems have no say in changing the system. This is a vicious cycle and the powerful people of science are either dumb enough not to see it or they actually want the flaws to perpetuate in order to retain their power. I am open to both the possibilities. If there is a third one that you can think of kindly let me know.

Here are the links where you can access my original manuscript along with one of the reviewers who has directly commented on it.

Another reviewer’s comments are on this link.

I leave it open for the readers to make their own opinions. Any comments are also welcome.

Science versus career: a trade-off

Within the last 24 hours I heard two complaints from two different students from very different institutions. I received a call last night from an agriculture student. He was in a tricky situation and wanted my advice. His dissertation work had ‘failed’. He wanted to develop a technology, a machine with some agricultural application, but it didn’t work. He had himself put in tens of thousands of rupees to make a machine, but it failed to work. Now his mentor was asking him to cook up some data and pretend that it worked, without which he said the degree won’t come. The application of the machine being season dependent, if he had to rework with refinement, he would have to wait till next year. The student was reluctant to give false data but simultaneously was terribly upset because not only he would lose the money he had put in, but also lose one full academic year.  

The other case was of a girl having completed her PhD desk work, was awaiting a paper to get accepted and preparing to complete the thesis. Typically at this stage, the post doc applications begin. The girl’s complaint was that during some phase of her work she had a major difference of opinion with her boss which strained their relations. Now she wasn’t sure whether her boss will give her a “good” letter of recommendation, without which she though she couldn’t get a post doc.

In both the cases I could sense that the students were terrified by the thought that their career could be completely destroyed in one stroke by their supervisor. In the first case the boy talked to me directly on phone and I tried to find out how thorough he was at his work. If the experiment failed, did he analyze and try to reason out why it failed? He said he was confident about his data and could say why their earlier thinking was wrong and why things cannot be done that way. I said, “I am ignorant about your field the only thing I would suggest a science student is this. You stand firm on your position and complete your report. Don’t cook up data only to please your boss. An analytical view of a failure is also a contribution to science. Try to convince your boss that you have sufficient work to make a dissertation, but avoid the temptation of cooking up data.”

The girl in the other case was not even ready to discuss her problems personally with anyone in academia. She was afraid that the ‘anyone’ may turn out to be a friend of her boss. If I get any chance to talk with her I will tell her more or less the same thing. If you think you are right, remain firm on your stand and don’t compromise your science for someone’s whim. If it is about post doc, communicate your problems frankly to your potential post doc mentor. It might work. There are alternative ways of doing good science and finding a career.

Frankly speaking I am not sure my solution will protect the careers of the two students, my solution is primarily to protect science from misconduct. Clearly in both the contexts there is an unfortunate tradeoff between doing good science and doing a successful science career. The two are way different and in instances like this, diametrically opposite.

My main concern goes much beyond the two cases. In science, one needs to be careful about experimental designs, accuracy, errors, biases, logical traps etc., but being confident about having done well on this front, it is necessary to stand firm on one’s position. Compromising on it to please someone like the mentor, the thesis examiner, the reviewer of manuscript or some established elite in the field is against the spirit of science. Conformity bias is a major hurdle in the progress of science and in both the cases this is exactly what is getting encouraged.

Now if the students’ fear is true, and complying with what their boss says is the only way of getting on to the career path, then the system is selecting against scientific spirit. My personal advice to the two students would be that if you are afraid of someone ruining your career, you are actually not fit to do science. You should find some other career for you, perhaps better than this. But what actually matters for them is ground reality and not my utopian advice. If taking a firm stand prevents them from getting their degrees or further positions, the system is filtering out at an early stage the right kind of mindset for science. If students are made to compromise their own stand in order to make a successful career, the future of science is bound to be bleak. With this trend academia would become completely devoid of the scientific spirit in no time.

I had related earlier one of my experiences as a reviewer ( In this case I differed from the authors in the interpretation of their experiment. Difference of opinion is fine. It’s a milestone in the path of healthy science. The authors could have counter-argued and defended their interpretation more clearly during revision and rebuttal. But I was the “reviewer god” of that moment. They didn’t agree with me but also didn’t want to displease the reviewer God. So in effect, they muddled the entire argument further and the residual clarity was also lost. I would have been happier to see them say “We beg to differ on this issue and we defend our stand with better arguments in the revised manuscript”. I would have recommend acceptance on such a stand, even if I didn’t agree. But the peer review culture is so degraded that many authors avoid any argument with the reviewer. This tendency makes publications easier but science more difficult.  Today’s science institutions as well as science publishing is taking such a shape that if you want to make a successful career in science then you need to compromise with the spirit of science.

The problem lies with the institutional culture, individual mindset and the norms of career path. Clearly there are individual mentors that encourage students to think and be independent at an early stage. But this is left to individuals. There is nothing in the system to ensure this. Solving individual cases or helping individual victims is not sufficient. The system and the norms of a typical career path need a rethinking.

Crowd-funding appeal to support farmers

using a novel ‘game theory’ based approach:

For the first time in my blog, I am making an appeal to contribute to crowd funding to support the pilot implementation of a novel scientific concept that has a direct application to solve a major problem in human-wildlife conflict. We play here a game with real money which solves a real life problem!!

The problem: In India, with its high population density and rich wildlife, there are large areas of inevitable human-wildlife co-existence. Strengthening agriculture in and around protected areas boosts conservation since it reduces people’s direct dependence on forest resources. Therefore farmer support needs to be a high priority conservation action. A major but largely overlooked problem is crop damage by wild herbivores. While compensating farmers for the damage caused by wild animals is an accepted principle in India, the process of assessing and compensating damage is fraught with a large number of practical difficulties and therefore has been ineffective.

A Novel solution: We came up with an alternative to traditional concept of compensation with a novel game theory based concept called “support cum reward” (SuR). The SuR amount to be paid to farmers is calculated as percentage based on the average loss in productivity over a group of farmers. Each farmer receives SuR as the percentage on his own productivity. Therefore while each farmer is supported by the average, a farmer showing high productivity in spite of the damage gets a higher reward. The data are self reported by farmers and endorsed by neighboring farmers. The unique game theory components ensure that honest self-reporting gets maximum returns. Since honest behavior is the most selfish behavior, there is little chance of anyone defecting from cooperation. This is a new game that is immune to cheating by any variant behavior. As a result the entire process can be automated through farmer friendly mobile apps so that the system becomes community operated with little need for central administration, auditing or policing. The design of the system has several built-in features of cross verifying data, automated detection and prevention of fraud, self-auditing and self-correcting.

A Prior small scale trial: SuR was implemented on a small scale (75 farmers) for three years in which the average farmer’s productivity increased over 2.5 fold. In this trial the SuR inputs of Rs 1.6 million increased the productivity by about Rs. 7 million. This phase of research was funded by Vidarbh Development Board, DeFries Bajpai Foundatin and NAAM foundation. This concept is appreciated in the scientific world as reflected by publication in one of the top journals in the field, and an international award by the Society for Conservation Biology.

Towards scale up: Now, the Forest Department of Maharashtra, has shown interest in conducting a medium scale pilot trial for 8 to 10 villages near Tadoba. We have signed a MoU with the Field Director TATR and the executive director of Tadoba Andhari Tiger Reserve Conservation Foundation. While the foundation takes the responsibility of managing the finance and partially funding the pilot, we still need to raise over Rs. 1.5 crore through crowd funding, CSR and other means. The medium scale implementation will pave the way for the Government to adopt a new policy to handle human-wildlife interaction.

Why citizen science: The nature of research is difficult to fit in the bureaucratic systems of Indian Institutions and universities going by my prior experience. Therefore myself and my team will work independently in collaboration with the farmers on the one hand and the Foundation on the other. This is a unique example of doing high quality science with direct community involvement, establishing a non-institutional model of doing science. Beyond handling people’s problem it is an attempt to raise an alternative model in which farmers including the illiterate ones turn into researchers and address their own problems themselves. It is not about ‘taking’ science to people it is about doing science with people.  Personally for me, this is going to be perhaps my career best science. You can donate starting from a few hundred dollars of few thousand rupees (no upper limit) at the link- Remember to go to the “other” option in the Type of donation window and write “For farmer support cum reward (SuR) fund”.

For more detailed information about SuR see links-शेतकरी-आधार-ईनाम-योजना-farmer-support-cum-reward/

Covid 19: Why can’t they do simple mathematics?

For a student of science like me, whatever is happening with the pandemic and whatever is being said reveals how human mind works, how we perceive science, how we ask questions and what satisfies our questions.

A number of statements are being made by the mainstream Covid epidemiologists which are actually not supported by data. The same data can be interpreted differently. The different possible interpretations need to be treated as competing hypotheses and tested by making additional differential testable predictions. This is the core method of science. But the human mind has not evolved to follow the scientific method and pursue truth. It has evolved to make stories. The stories need to satisfy some audience. The attempt of an investigator is to satisfy his/her present audience with minimum effort. Different audiences have different satisfaction thresholds. If your story satisfies your audience, you stop there. You stop looking for alternative interpretations. You don’t need to test them. You don’t need to recheck whether your story is consistent enough with evidence. Whether there are any anomalies and whether they are serious enough to raise doubt on your story is no more a concern as long as nobody challenges your story. If someone challenges the story the first line of defence is to say the person is not credible, ignore him or better brand him as ‘anti-science’. Such branding works most of the time, so that you don’t really have to take trouble to pursue scientific method further. Scientific solutions are costlier. Social solutions are cheaper.

Take the example of the new wave which is said to be caused by the variant omicron. Is this a well tested hypothesis? By the methods of science that we teach undergraduates, any hypothesis can be tested against a null hypothesis. Different variants keep on arising and vanishing in a population owing to chance as well as by selection, and selection can happen due to not one but multiple reasons. In viruses, there is selection within a cell, during cell to cell transmission, there is selection on dose of the virus being transmitted, time for which the host remains infective, time for which viral particles survive outside the host, how the virus elicits immune response, whether and how virus evades host immunity and so on. The selection is necessarily multi-level with additive as well as multiplicative components and is really a challenge to selection dynamics. Further there can be trade-offs between any of these. Natural selection on viruses is much more complex than the prevalent naive thinking that a more infectious virus will win the race.

As the wave goes up and down, new variants keep on arising. Many variants increase their abundance while the wave is downwards. So new variants keep on arising during any part of the wave and by chance some variant may happen to ride a rising wave. This should be the null hypothesis, only by rejecting which we can say that a given variant is certainly responsible for a wave. I could not find any such analysis in literature but a story seems to be accepted that the new wave is because of omicron. The increase in proportion of omicron is not always accompanied with increased transmission. For example, in Russia, between 29th November 2021 and 11th Jan 2022, the Omicron proportion increased from negligible to 50 % while mean number of cases per day came down by more than half. Omicron accompanied a downward and not upward trend in total number of cases.

Even in countries where omicron accompanied a rise in the wave, omicron does not account for the rise. In India, for example between Dec 29th and Jan 20th the incidence rose about 40 fold and omicron proportion increased from negligible to 77%. By simple arithmetic, a rise to 77% can explain only about   4 fold increase in total incidence. During this time the delta variant incidence also increased by over 9 fold. If the new wave was caused by omicron, why did we see the delta cases going up 9 fold? This is simple arithmetic that we teach in secondary schools. Perhaps for experts in this field using simple mathematics is too below their dignity. They have to use sophisticated models to impress everyone. Simple arithmetic poses a major anomaly for the hypothesis that the new wave is “caused” by omicron. Data only show that omicron is associated with the new wave, that too in some countries, not everywhere. It is equally likely that the selective environment during the new wave allowed the spread of omicron. So the wave is the cause and omicron the consequence. Alternatively the association is only coincidental and there is no causal association between the two. Unless such alternatives are considered as competing hypotheses and ruled out, ‘omicron caused the new wave’ is not a scientific statement, by the core principles of science. It’s only a story that has convinced most people, therefore it is considered scientific as of today.

This is how science works most of the time. It is a complex social process that sometimes, particularly when convenient, uses the fundamental principles and methods of science. The methods of science is the tool used by people whose prejudices, interests and agendas decide the emerging inferences.

Then what caused the new wave? There are several alternative possible reasons. We showed with a model earlier that the wave pattern is possible by considering immunity as a continuous rather than a binary variable. In reality, immunity IS graded and not binary. By this model, waves arise and wane even without a new variant. So it is very much possible that waves arose as a part of the population dynamics of what we called in the model as ‘small immunity effects’. Alternatively it is also possible that the vaccines give only systemic immunity, but respiratory mucosal immunity has a different set of mechanisms which the vaccine administered by injection does not strengthen much. As a result when the low tide lingers around for sufficiently long period people tend to lose mucosal immunity, but systemic immunity is still there. Therefore a new wave begins with whatever variant is around, but does not lead to serious symptoms among the vaccinated. If this hypothesis is true, we should see increase in cases of all prevalent variants, though not to the same degree because different variants have different competitive abilities. Also even where the incidence is declining, the more competitive variant will become commoner. I am not saying this IS the reason. I am saying that being open to different possibilities and testing them with differential predictions is how science should work, but during Covid times scientists themselves seem to have forgotten the methods of science. I am happy that the Covid epidemiologists are providing a science teacher like me several examples to demonstrate how science should be and should not be done. It is also enriching in me the curious student of social psychology of science. It is interesting to see how the community of scientists actually works besides the principles of science.

Covid 19: Evolutionary interactions between vaccine, immunity and virulence. 

The Covid-19 picture is changing rapidly and if viewed with an unprejudiced mind, keeping several alternative possibilities open, a clear interpretation is emerging. There is a new wave spreading world-wide with unprecedented numbers but at the same time the proportion of hospitalizations and deaths are far less than earlier waves. Why is it so? There are two prevalent explanations which are mutually contradictory. On the one hand this is said to be an epidemic among the unvaccinated; on the other hand the reduced severity of symptoms and lower hospitalization and mortality rates are also credited to the vaccines. Both cannot be simultaneously true, if we do a simple calculation.

The global trend in the ratio of number of deaths and number of cases – a seven day running average.

Take the case of UK. The ratio of new cases to new deaths has come down from an average of above 10% between April and June 2020 to an average of 0.15 % between December 2021 and today. That is two orders of magnitude. If we have to ascribe the credit for reduced severity to vaccines alone, we will have to assume that in the unvaccinated, the severity remains unchanged. Going by this assumption, and given that 70% of the population of UK is fully vaccinated, for bringing down the mortality by over 50 fold it is necessary that vaccinated people are about 20 times more likely to be infected than unvaccinated people. Since this is absurd, we need to accept that the severity of infection in the unvaccinated has also come down substantially. This can either happen because the unvaccinated have also become immune by natural infection, or because the virus has mostly lost its virulence.

In order to differentiate between these two, we can have a look at Australia which also has about 70 % population vaccinated. But Australia had successfully kept the infection away until recently and therefore the unvaccinated are unlikely to be immunized by natural infection. When the number of cases is small, calculation of death rate is subject to large stochastic fluctuations. So we take only the period in which there were more than 100 deaths per week. This was the situation in Aug-Sept 2020 when the ratio in Australia was over 10 %. Then there was a long time in which there were zero or negligible Covid cases and deaths in Australia. So the population had little chance of acquiring immunity by natural infection. This situation changed again only after October 2021, but now the death proportion was much lower, about 0.6 % and between October 2021 and today it further declined to 0.05 %. Similar to UK if this is to be explained by vaccination alone, we will have to assume that vaccinated people are about 70 times more likely to get infected. Since this is unlikely, it is clear that the reduction in severity and mortality is not explained by vaccination and naturally acquired immunity together. The virus has indeed evolved towards reduced virulence. It was round about 20 times more severe than flu in the beginning which has come down to about 2 to 3 times. It is still more severe than common cold and flu but is moving rapidly to become just another.

Evolution will reduce the virulence of the virus was my clear prediction right from May-June 2020. The literature of evolution of virulence is full of mutually contradicting and confusing arguments. But the case with Covid has been very clear. Multiple studies showed that there was poor correlation between severity of symptoms and viral load. Virulence can give a selective advantage to a virus only if it is tightly correlated to the number of virus particles being shed by the host. If correlation with numbers is poor, there is no selective advantage in being virulent. On the contrary, virulent variants are more likely to face quarantine and thereby restrict their transmission. A milder strain allows the host to move around in the population and thereby spread it more widely. The reduction in severity of symptoms, hospitalization rates and mortality was a conspicuous trait in the beginning. But then it appeared to stagnate and had some hick ups for some time. I was perplexed by this trend but a couple of possible reasons soon became apparent.

There is another, more subtle contributor to selection for lower virulence. The immune status of the host exerts a strong selective pressure on pathogen virulence and this has received little appreciation in virulence literature. Immune response is costly in terms of resource allocation as well as the potential damage to tissues through heightened inflammatory and oxidative components of the process. Therefore it is not wise for the body to launch an all out immune response for every pathogen encountered. At times, particularly for milder pathogens, the cost of the immune response might be greater than the cost of being infected. The host therefore should make a judgment of the invisibility or virulence and accordingly optimize the immune response. The response given to a highly virulent pathogen must be of high intensity, but that given to a milder pathogen should be of minimum necessary intensity. There is some evidence that this is what the body actually does. It gives a high intensity immune response if and when a normal inhabitant turns virulent. For opportunistic pathogens living on the body, exposure to the organism was always there, but what changed was the level of invasion. Even in Covid data, the immune response obtained after a severe infection has been shown to be more intense than an asymptomatic one.

This exerts a differential selective pressure. If the host has a good immune infrastructure and possibly immune memory already existing against the pathogen, a virulent variant will evoke a strong immune response and thereby cause its own destruction. A mild variant on the other hand, may not evoke a strong immune response and thereby may get away causing a mild infection and spreading to a few more individuals. Therefore as the population immunity increases, the pathogen evolves to be milder. This can happen by naturally acquired as well as vaccine induced immunity. Therefore we witness that after a substantial population got vaccinated, the downward trend in virulence got steeper once again.

The concept of optimizing immune response can potentially answer one more question. Why in Covid the vaccine induced immunity appears to be short lived, contrasting with small pox. Corona viruses are seldom that virulent. Most are mild. Pox viruses can be very deadly. It is possible that our systems have evolved to invest less in immunity against Corona viruses and more against poxviruses. This hypothesis is worth exploring further.

But why did the declining virulence appear to stagnate in the middle stages of the pandemic? I think the possible reason is that we blunted natural selection by our own preventive strategies. Selection for milder strain is strong if the severe cases are effectively quarantined and milder ones are allowed to mix in the society. But owing to the contact tracing approach, we tried to quarantine everyone exposed, reducing seriously the selective advantage for the milder varieties. We also reduced our general immunity levels by the extra precautions taken, including masks and sanitizers. There have been reports that in countries where the preventive measures were very successful initially, infections by common endemic and seasonal mild strains of viruses suddenly started needing hospitalization. This is an indication that the preventive measures have actually been creating an immune bankruptcy or what has been called in published literature an immunological debt over time. What can be beneficial in the short run can become counterproductive in the long run. Note that ALL studies showing the beneficial effects of masks are short term studies 3 or at the most 5 months. Nobody has followed the long term effects of regular use of masks. This has never been a part of the mainstream thinking in preventive medicine, but evidence for preventive measures undermining immunity has been published in the context of many different diseases multiple times.

So, the vaccines have been useful in an unexpected way. They don’t seem to have prevented the spread of infection very efficiently. But they appear to have helped in creating a stronger selective pressure for decline in virulence. On the other hand it is quite possible that masks, that were helpful in the short run, may have turned counterproductive by changing the intra-host selective environment. Ultimately Covid is bound to become just another common cold virus and the progress in that direction is clearly visible. There is no other likely fate of the pandemic. The question is whether we could have facilitated the rate of this evolution. My gut feeling is yes, we could have, by implementing better designed and carefully optimized preventive measures. But not only data needed for this optimization is absent, even such a concept is absent, so systematic studies in that direction are not even expected to happen. Evolution today is rich in molecular data but that comes as a cost of deteriorating insights into natural selection. During the Covid saga, there were plenty of talks and huge data about the mutations and the variants and their transmission, but little insights into how selection worked on the mutants being generated. There were some half baked ‘evolutionary’ statements. Someone said that by the preventive restrictions you can keep the viral population limited and thereby minimize mutants arising. But the question whether viral evolution is mutation limited or selection limited was never critically examined. Sound evolutionary thinking needs being open to alternative possibilities, insights into possible selective forces and a keen eye on the patterns in data. If such thinking becomes a part of preventive medicine, I am sure it will make handling of future epidemics more efficient.

The epidemic of scientific misconduct: an innocent question

It’s not a new virus. Various types of frauds have been there throughout the history of science. The fake fossil human skull famous by the name Piltdown man created its own history. Some experimental results by great personalities such as Gregor Mendel and Jacques Monod are said to be “too good to be true”, i.e. so well fitting their hypothesis that such a result is statistically highly unlikely given the inherent biological variability. It is possible therefore that they themselves, or someone obliging them manipulated results or cherry picked only the favorable ones. There can be many more examples that never got exposed. But in spite of such examples the core spirit of science has largely survived and maintained its reputation.

However, the rate at which misconduct is mounting today seems to be unprecedented. There is reproducibility crisis, doubts raised about designs and conduct of clinical trials, statistical analysis twisted to support the favored hypothesis, manipulated images and cooked up data. It is difficult to decide whether the frequency of scientific misconduct has increased or only the rate of getting exposed has. The rate at which image manipulations are getting detected is due to the efforts of groups like ‘pubpeer’ (, Image manipulation is only one type of fraud. Currently most of the flags raised are based on detecting image manipulation. Many other types of frauds may have just escaped because we don’t have tools to detect them. If this is true, the actual frequency of misconduct must be substantially higher. ‘Retraction Watch’ ( makes data on over 30,000 retractions and the reasons behind them accessible to people. Retractions on getting the misconduct exposed include papers by reputed laboratories and mentors. Occasionally the responsible people have to pay the cost of the deed through their nose but many others appear to escape more or less unhurt. The culprits may not pay the cost but science may be paying a big cost in terms of its reputation. Certainly I find it hard to prevent myself from losing faith in published science and I am not the only one. Richard Smith, the former editor of British Medical Journal, remarks that it is time to assume that all health research is fraudulent until proven otherwise!! (

There have been many discussions on how to prevent the scientific frauds of various kinds. People have been debating the pros and cons of better ethics education, institutional mechanisms of vigil , strict action and punishment as deterrent and so on. But I am troubled more by another innocent but unpleasant question that keeps on peeping in my mind in spite of my repeated attempts to suppress.

The high frequency of retractions makes me wonder about how science works today. I thought science works by building on prior knowledge and working the way ahead. In my mental model of science, every piece of evidence, every bit of data, any new concept, model, analysis is crucial for progress. One published piece of work lays the foundation of further work and so on. If this was true, any paper retracted would have made some concept collapse, some paradigms failed, some lines of work given up, some technologies defunct and so on. But I am surprised that following over 3000 retractions per year there is hardly any collapse seen, no fundamental change in the direction of work, no reconsideration of the existing paradigm, no rethinking of any prevalent theory. If retraction of thousands of papers has no major effect on science, it only means that these papers never had any relevance to science. Their being in a state of ‘published’ or ‘retracted’ makes no difference to the field. Perhaps they are being published only for the benefit of the authors in building their CVs and getting better positions. If they really made a difference to the field of science, their retraction should have affected the field quite badly, but that is not the impression one gets after looking at the work of pub-peers and retraction watch. Thousands of papers have been retracted but no scientific theory has collapsed or no technology has been withdrawn.

If papers are irrelevant, so must be the career of a researcher. If in judging the career of a researcher, we go by the number of such publications and weigh by impact factors and so on, we are counting the irrelevant. This way we are building more and more irrelevant science in our institutions. No wonder if the institutions themselves become irrelevant soon. They already have to a large extent, as the apathy of common man, government and media shows.

I still hope and believe that interesting, fundamental, relevant and important science is being pursued somewhere. But it may not be with the big people working in the prime institutions of science and publishing in big journals. It may be happening in some obscure lab somewhere, some teacher with a handful of undergraduate students, some farmer, some uneducated, illiterate, humble, unnoticed individuals in some corner of a third world addressing a basic question. May be that kind of science will count ultimately, some day, some time. Let us wait till them.

What else can we do?  Tell me if you know.

Time to revive science

Now that Covid-19 is increasingly taking the shape of a milder, stable and endemic disease, the panic, fear, paranoia response should be giving way to more scientific and analytical thinking. It is understandable if under the grip of fear open minded and analytical thinking becomes impossible. This certainly appears to have happened throughout the pandemic. Much of the information and advice being spread across official channels was inadequately evidence based, without much scientific foundation and at times inflicting more damage than mitigation. Simultaneously a number of known and previously well demonstrated principles were ignored without reason. Now that the global mind should be in a better condition to sustain balanced and logical thinking, science should be back on the central stage and should replace rhetoric. It may not be able to rectify the damage already done, but rethinking at this stage will enable us to learn from history and avoid repeating the same mistakes in case of a future epidemic.

The loopholes that I could visualize in mainstream science during the intellectual pandemic included being closed to alternative possibilities, forgetting prior knowledge and wisdom, giving some ‘explainawaytions’ for the observed patterns without a proper hypothesis testing approach, projecting a rhetoric as scientific truth, black and white vision i.e. inability to perceive gray shades of reality, getting blinded by partial or inadequate knowledge, forgetting the importance of contextuality and believing in one size fit all, fragmented vision i.e. the inability to put together different currencies of cost benefits in making a public health policy and so on. In the long run, the response of public health authorities to the pandemic should become a text book example of how science should not be done. Here I will describe only one example of how the mainstream has failed to think about alternative possibilities.

The core of scientific methods is hypothesis driven and using evidence for testing differential testable predictions of alternative hypotheses should be the core scientific method used. This approach was conspicuously lacking during the entire course of the pandemic, and it was lacking at each of the three steps. First, alternative possibilities need to be recognized; second, ways to differentiate them should be sought and third, attempt to falsify or support one or more of them should be made.

Our earlier paper published in PeerJ was one of the few such attempts ( seen on the stage of science during the pandemic. There are many more examples. Another paper on a slightly different approach to modeling an epidemic just appeared as a preprint (, represents a slightly different way of thinking that raises alternative possible reasons for many of the observed patterns in the pandemic. A predominant and consistent pattern across the globe has been that the peak and subsequent decline of a surge happens at orders of magnitude lower levels than the predicted herd immunity threshold. By the prevalent epidemiological thinking the decline in incidence starts when a threshold proportion of people become immune. This herd immunity threshold depends upon the rate of transmission or R0. By the observed trends in the pandemic, herd immunity was expected to be somewhere between 50 to 80% of the population. But in reality every wave started a downward trend at a very small fraction of the population getting infected. Also after reaching the threshold, the disease is expected to decline rapidly to approach eradication. But we actually saw it stabilizing at a variable and unpredictable level for some time followed by a new surge.

In our model, this is precisely what is expected to happen. Our model differs from the classical model which treats immunity as a binary variable. In classical models any living individual is susceptible, infected or immune at a given time. Our model treats immunity as a continuous variable instead and incorporates small and subtle changes in immunity. Many such small immunity effects (SIEs) have been demonstrated previously. For example respiratory infections by other viruses contribute some cross immunity. When one gets a small exposure to the virus not sufficient to cause active infection, it may cause a small increment in immunity. Repeated subclinical exposures are known to boost immunity as demonstrated with other viruses previously. In a population, while some individuals get doses of the virus large enough to cause a clinical course of infection, many more get a lower dose that fails to cause an obvious infection but the immunity is pushed a little bit upwards. This change may be one or two orders of magnitude smaller than immunity acquired by active infection or vaccination, but this small increment in immunity can bring about a big change in the epidemiological picture. It is long known that the relationship between dose of pathogen and probability of infection is sigmoid. Because of the sigmoid nature, at times a small increment in immunity increases the chance of escaping infection substantially. People with such small increments can escape infection for the time being but the effect being small wanes faster. A small decline in immunity in the absence of repeated exposure is also a known SIE. As a result the first wave is arrested rapidly but then the immunity level of such people can also decline fast as the wave recedes. This creates a part of the population susceptible again and a second wave may begin. This wave pattern does not necessarily need new variants to start a new wave. New variants can nevertheless ride a wave. So the shape of the epidemic is largely decided by the small immunity effects. We only included those effects that have been demonstrated before, at least qualitatively.

Currently we have no tools to monitor the mechanisms of SIE at a population level. But the SIE model is able to reproduce/explain/mimic almost all the patterns seen in the pandemic with or without new variants. A model is not a proof of what happens in reality, but a model can raise new possibilities and if they explain the real life patterns better, they need to be considered seriously.

There are some prevalent explanations for the dwarf peaks and repeated waves which need to be considered as alternative hypotheses. One for the reasons given for dwarf peaks is that the non-pharmaceutical measures applied such as masks and lockdowns could have controlled the transmission effectively. This is testable since it makes predictions that are different from the small peaks caused by SIE effects. One is that whenever lockdowns with different stringencies are applied, the incidence curve should show a decrease in slope and whenever lifted show increase in slope. We have analyzed this previously ( to show that the effect is very weak or marginal. 31 % of the times the slope has actually increased after applying restrictions and 45.4 % of the times it has decreased after lifting restrictions contrary to expectation. Even by a liberal estimate not more than 9 % of the lockdowns have reduced the transmission above what is expected by chance. So masks and lockdowns clearly fail to account for the universality of dwarf peaks. There is another prediction that is yet to be tested rigorously. If the decline is caused by population immunity, either conventional or the SIE, then we observe a symmetry in the shapes of the peaks i.e. a rapid rise is accompanied by a rapid decline, slow rise goes with a slow decline. This is because a rapid rise is accompanied by rapid changes in population immunity which is responsible for the decline. This type of symmetry is not expected if lockdowns arrested the peaks and caused a decline. In fact, factors such as host population density that facilitate a sharper rise will make a steep decline more difficult, taking the shape farther away from symmetry. Analysis of symmetry of peaks can be used to differentiate between whether lockdowns caused the dwarf peaks or small immunity effects. The symmetry analysis is yet to be done elaborately, owning to its statistical complexity, but one can glance through the incidence curves of all countries to realize that over 70% of the peaks are quite symmetrical. So the small immunity effects are more likely to be the cause of dwarf peaks than the lockdowns and masks.

The repeated waves can be because of the small immunity effects and/or new variants evading immunity against prior variants. Here too we can make differential testable predictions. If the virus evades prior immunity then the front line workers that are more exposed to infection should show greater incidence just as in the first wave. On the other hand, if the SIEs are mainly responsible for the second wave, by the model prediction the second wave should disproportionately affect people that are less exposed or more protected. Testing this prediction also needs rigorous retrospective study, but the impression expressed by many doctors and epidemiologists has been that in the second wave more of people protected in the first wave have been affected.

Testing differential predictions is not yet complete but SIE looks to be more likely explanation of the dwarf peaks and repeated waves. The SIE model also expects a surge after majority of the population is vaccinated, which has been a repeated pattern hard to explain. So we need to be open to the SIE effects as an alternative way of thinking, but the small immunity effects have never been a part of mainstream thinking.

The implications of SIE are serious and shake the fundamental assumptions behind public health policies applied during the pandemic. The SIE model shows that by arresting the first wave, the second wave becomes more likely. Particularly in populations that may have a higher background immunity contributed by cross reactivity to other respiratory viruses, masks and lockdowns can be counterproductive in the long run. They can cause a second wave that is more destructive than the first with or without a new variant. The second wave predictions of the model appear to match with that in India in particular. It is likely that the severity of the second wave was because of the protection measures turning counterproductive. Today we do not have convincing evidence either way, but the certainty about masks being always protective is definitely under serious doubt. It is important to realize that all the studies showing the efficacy of masks are short term studies never going beyond 3-5 months. Masks can turn counterproductive because they interfere with the protective small immunity effects but allow small accumulating decline in immunity. The model also shows that masks and vaccines are antagonistic in their net effects. It may be a better policy to contraindicate masks after vaccination. CDC actually recommended this but the reasons for this recommendation were not made clear. Therefore whether masks are good in the long run is an open question. Public health policies without seriously addressing such questions should be called pseudoscience.

Many more flaws in the mainstream beliefs behind public health policies and the science behind it can be pointed out. Although huge amount of money went into research, we know very little about the mode of transmission, which is of central importance for an epidemic. Huge data on mutations and variants was generated but little efforts to relate it to clinical picture. It is impossible that so many mutations did not affect the behavior of the virus, but people handling genome data have no information on disease behavior and clinicians kept on wondering how so much of sequence data would help them. Interestingly the evolution paradigm itself seems to have changed in that everyone talks about mutations now but hardly any one talked about the complexity and subtlety of selection on the virus. Viral evolution during an ongoing pandemic is a multi-level selection problem and interesting data came up during the pandemic which would take the debate on multi-level selection substantially forward, but no evolutionary biologists seem to have used it so far. We do not have reliable methods to quantify ‘infectivity’ and ‘virulence’ because both can alter substantially by SIEs and any estimates without considering SIEs can be completely misleading. But without any reliable methods of estimation everyone kept on talking about more infective and immunity evading variants. Contradicting this, the vaccine group kept on asserting that vaccines are effective against all variants so far. I have seen no attempts to resolve this contradiction. The question whether viral evolution during a pandemic is mainly mutation limited or selection limited has not been even asked, forget addressed. The inadequacies in our understanding of immune mechanisms are exposed even more. Inadequate knowledge is not a crime or a flaw, but denying the inadequacies and pretending or making claims as if everything is known is.  All this could be said to be inevitable under the pressure, panic and paranoia, but now is the time to do more sound science. This is the right time to strengthen real and unbiased science forgetting who was right and who was wrong, welcoming cross questioning and open mindedly addressing alternative possibilities so that we don’t repeat the same mistakes in a future threat of another respiratory virus.

Greater the volume of scientific literature, slower the progress of science:

In a recent discussion with someone having spent his entire life in science, and refusing to dilute his commitment even after retirement, an interesting issue came up. The volume of published literature is increasing rapidly making the life of interested readers more and more difficult. A reader has to make a quick decision as to whether to read a paper by quickly looking at the title and may be just first and last line of the abstract. This is the most critical step and a bottleneck in science communication. How does a reader take a decision to spend a little more time on the paper? (forget complete reading). Of course readers vary and there is no one rule fit all. But in our discussion we stumbled upon one such rule that seems to work quite often. Whenever there is something in the title and abstract that sounds contradicting the reader’s logic/thinking/prior knowledge/belief/interest or whatever, it is immediately swept aside. This might be either because there is an inherent resistant to paradigm shifting ideas as Thomas Kuhn says, but also because there have been too many false alarms. There have been too many writers “trying to prove Einstein wrong, prove Fermat’s last theorem or have a method to square a circle etc, so for those who have far too many things to read and too little time, a quick decision has to be made and making a negative decision is statistically correct” (His words, somewhat rearranged). This means that if a genuine breakthrough is being published, it is least likely to be read by anyone.

When I examined my own behavior, I thought I would do the reverse. If there is something deviating from the trodden path towards pre-conceived goals, I would read it with priority and more curiosity. Obviously readers differ in their choices. One (rather oversimplified) classification of readers is that there are explorers versus track racers. Explorers are typically slow, somewhat aimless, more curious, with little preconceived ambitions and aspirations, driven more by curiosity than by the desire to succeed in something. They will be attracted by titles and abstracts sounding off-beat, challenging mainstream thinking, iconoclastic, non-conformist. The other class, the track racers are committed to a small field, have a vision as to what they have to achieve in as little time as possible, they want to be and often are more productive in their published output and so on. They will quickly and efficiently decide what they don’t want to read. They will quickly short list the articles that they can potentially cite. For citing something it is often a waste of time to read it entirely. But they know quite well what to read and what to skip. So they can make best use of their time.

Potentially both the classes have differential importance to the field of science. A good combination of the two would benefit science. But I am afraid, the frequency of explorers is declining as I can see. The reason is the process by which individuals are selected to enter the field of science. They are selected by ‘merit’. At the UG stage, merit is first screened by success in the Board and University exams, only secondarily by interviews etc. At a later stage selection is based on where you graduated, whose lab, whose recommendation you carry, where did you publish, how many papers, with whom and so on. Here to actually read papers of the faculty or post doc candidate is not affordable because there are so many applicants and judging someone by reading his work is just out of question. In this process the track racers almost always outcompete the explorers. As long as the selection procedure does not change, the publication smartness along with some luck will always weigh heavily over exploration, novelty and originality of thinking.  As a result, I suspect, explorers are getting extinct from the field rapidly.

This extinction would further make reading of off-beat papers even rarer. Trodden paths will become even stronger and inescapable. There will be greater and greater career success with lesser and lesser science. Is there a way out?

I am not a pessimist.

The way out as I can see is to do science outside the institutional and academic framework. Strengthen citizen science. It is not difficult to do good science outside the institutional framework. But it is more difficult to publish in the mainstream journals having the prevalent  brahminical culture. The word brahminical here does not refer to caste by birth, religion etc. It refers to monopolizing science by certain institutions; denying science coming from elsewhere and in any other form; refusing to recognize anything as science unless published in peer reviewed journals; the exorbitant author charges that make it impossible for a citizen to publish his science in the mainstream journals. There is one more important difference. Citizen science demands that you read it. In brahminical science, just that it is published in a peer reviewed journal is enough. You don’t have to read it yourself and decide anything. Interestingly, editors and peer reviewers are also readers first and they follow the same culture. They too have to take a quick decision, and not reading it is the quickest way of judging. The review process is opaque. So a paper has gone through peer review can only be called a belief. Anything that is not transparent is not scientific by any standard. But once the label of peer reviewed journal is obtained, it is believed based on the label itself. Throughout this process rarely anyone reads a paper completely. If it is labeled Ganga water, it must be “purer” than your tap water by all means.

There can be exceptions of course. I was told, that late Prof. S. Chandrashekhar of Chicago would read each and every paper submitted to the ‘Astrophysical Journal’ from beginning to the end, during the many decades for which he was the editor. Some individuals of this species may still be there but overall I suspect, this species is near extinction. A culture of reading with curiosity, reading with interest should be central to science. But the huge volume of literature being published in any field is making this impossible. That’s why my feeling is that the greater the volume of literature published, slower will be the conceptual progress of science. It will swell more and more, no doubt. But swelling is not progress, edema is not growth.