The<unk>  may be triggered by the "generation of new data and scientific publications at an unprecedented rate" that leads to the "desperation to publish or perish" and a failure to adhere to good scientific practice. Historical and sociological roots Predictions of an impending<unk>  in the quality control mechanism of science can be traced back several decades. Derek de Solla Price—considered the father of scientometrics, the quantitative study of science—predicted that science could reach "senility" as a result of its own exponential growth. Some present day literature seems to vindicate this "overflow" prophecy, lamenting the decay in both attention and quality. Historian Philip Mirowski offers another reading of the<unk>  in his 2011 book Science-Mart: Privatizing American Science. In the title, the word Mart is used by Mirowski as a metaphor for the commodification of science. In Mirowski's analysis, the quality of science collapses when it becomes a commodity being traded in a market. Mirowski argues his case by tracing the decay of science to the decision of major corporations to close their in-house laboratories. They outsourced their work to universities in an effort to reduce costs and increase profits. The corporations subsequently moved their research away from universities to an even cheaper option – Contract Research Organizations. Social systems theory, as expounded in the work of German sociologist Niklas Luhmann, inspires a similar diagnosis. This theory holds that each system, such as economy, science, religion or media, communicates using its own code: true and false for science, profit and loss for the economy, news and no-news for the media, and so on. According to some sociologists, science's mediatization, its commodification and its politicization, as a result of the structural coupling among systems, have led to a confusion of the original system codes. If science's code of true and false is substituted with those of the other systems, such as profit and loss or news and no-news, science enters into an internal<unk> . Economist Noah Smith suggests that a factor in the<unk>  has been the overvaluing of research in academia and undervaluing of teaching ability, especially in fields with few major recent discoveries. Publish or perish culture in academia Philosopher and historian of science Jerome R. Ravetz predicted in his 1971 book Scientific Knowledge and Its Social Problems that science—in its progression from "little" science composed of isolated communities of researchers, to "big" science or "techno-science"—would suffer major problems in its internal system of quality control. Ravetz recognized that the incentive structure for modern scientists could become dysfunctional, now known as the present publish-or-perish challenge, creating perverse incentives to publish any findings, however dubious. According to Ravetz, quality in science is maintained only when there is a community of scholars, linked by a set of shared norms and standards, who are willing and able to hold each other accountable. Philosopher Brian D. Earp and psychologist Jim A. C. Everett argue that, although<unk>  is in the best interests of academics and researchers as a group, features of academic psychological culture discourage<unk>  by individual researchers. They argue that performing<unk> s can be time-consuming, and will take away resources from projects that reflect the researcher's original thinking. They are harder to publish, largely because they are unoriginal, and even when they can be published they are unlikely to be viewed as major contributions to the field. Ultimately,<unk> s "bring less recognition and reward, including grant money, to their authors. A major cause of low reproducibility is the publication bias stemming from the fact that statistically insignificant results and seemingly unoriginal<unk> s are rarely published. Only a very small proportion of academic journals in psychology and neurosciences explicitly welcomed submissions of<unk>  studies in their aim and scope or instructions to authors. This does not encourage reporting on, or even attempts to perform,<unk>  studies. Among 1,576 researchers surveyed by Nature in 2016, only a minority had ever attempted to publish a<unk> , and several respondents who had published failed<unk> s noted that editors and reviewers demanded that they play down comparisons with the original studies. An analysis of 4,270 empirical studies in 18 business journals from 1970 to 1991 reported that less than 10% of accounting, economics, and finance articles and 5% of management and marketing articles were<unk>  studies. Publication bias is augmented by the pressure to publish and the author's own confirmation bias, and is an inherent hazard in the field, requiring a certain degree of skepticism on the part of readers. Certain publishing practices also make it difficult to conduct<unk> s and to monitor the severity of the reproducibility<unk> , for oftentimes the articles do not come with sufficient descriptions for other scholars to reproduce the study. The Reproducibility Project: Cancer Biology showed that of 193 experiments from 53 top papers about cancer published between 2010 and 2012, only 50 experiments from 23 papers have authors who provided enough information for researchers to redo the studies, sometimes with modifications. None of the 193 papers examined had its experimental protocols fully described and<unk> g 70% of experiments required asking for key reagents. The aforementioned study of empirical findings in the Strategic Management Journal found that 70% of 88 articles could not be replicated due to a lack of sufficient information for data or procedures. In water resources and management, the majority of 1,987 articles published in 2017 were not replicable because of a lack of available information shared online. Questionable research practices and fraud Questionable research practices (QRPs) are intentional behaviors which capitalize on the gray area of acceptable scientific behavior or exploit the researcher degrees of freedom (researcher DF), which can contribute to the irreproducibility of results. QRPs do not include explicit violations of scientific integrity, such as data falsification. Researcher DF are the often-arbitrary choices made throughout the experimental process which can be opportunistically exploited to result in false-positives and inflated effect size estimates, contributing to the irreproducibility of results. Researcher DF are seen in hypothesis formulation, design of experiments, data collection and analysis, and reporting of research. Some examples of QRPs are data dredging, selective reporting, and HARKing (hypothesising after results are known). In medicine, irreproducible studies had six features in common. These included investigators not being blinded to the experimental versus the control arms, a failure to repeat experiments, a lack of positive and negative controls, failing to report all the data, inappropriate use of statistical tests, and use of reagents that were not appropriately validated. The US Food and Drug Administration estimated that 10–20% of medical studies in 1977–1990 involved questionable research practices. A 2012 survey of over 2,000 psychologists indicated that over 90% of respondents admitted to using at least one QRP, although the methodology of this survey and its results have been called into question. Psychology has also been at the center of several scandals involving outright fraudulent research, such as scientific fraud by social psychologist Diederik Stapel, cognitive psychologist Marc Hauser and social psychologist Lawrence Sanna. Despite these scandals, scientific fraud appears to be uncommon. In 2009, a meta-analysis found that 2% of scientists across fields admitted to falsifying studies at least once and 14% admitted to personally knowing someone who did. Such misconduct was, according to one study, reported more frequently by medical researchers than by others. Statistical issues According to a 2018 analysis of 200 meta-analyses, "psychological research is, on average, afflicted with low statistical power", meaning that most studies do not have a high probability of accurately finding an effect when one exists. Findings from original studies which have low power will often fail to replicate, and<unk>  studies with low power are susceptible to false negatives. Low statistical power is a substantial contributor to the<unk> . Within economics, the<unk>  may be exacerbated because econometric results are fragile: using different but plausible estimation procedures or data preprocessing techniques can lead to obtaining conflicting results. Base rate of hypothesis accuracy Philosopher Alexander Bird argues that high rates of failed<unk> s can be consistent with quality science. He argues that this depends on the base rate of hypotheses - a field with a high rate of incorrect hypotheses would see a high rate of failed reproductions. Given the parameters of statistical testing, 5% of studies testing incorrect hypotheses would be significant (a false positive). If there are almost no correct hypotheses (true positives), then the false positive findings would outnumber the true positives. When trying to replicate these results, a further 95% of the false positives would then be identified, resulting in a high number of failed<unk> s.
Replication crisis
Breakthrough infection
Pandemic fatigue
Exacerbation
Second-impact syndrome
Ischemic cascade
Distributive shock
Cryptic shock