Suppression of environmental health scientists: real-world examples as a basis for action
Bibliographic record
Abstract
Pressures on epidemiologists, toxicologists, and on public health scientists to suppress their work are known to occur worldwide. In this article, we share six stories from environmental health scientists about the pressures they faced in their jobs after bringing public health problems to light. The method used to document each of the stories was to invite scientists who attended meetings of the International Society for Environmental Epidemiology to tell their own stories of having experienced research suppression. We then extracted the salient features of each experience into a coherent story, providing references as corroboration where possible. The specific purpose in going public with the six stories presented in this article is to open a conversation to better equip colleagues to stand up to pressures to suppress their work. By publicly sharing the pressures experienced by these scientists in attempts to suppress their scientific work, including intimidation, harassment, threats and/or bullying, other scientists may be better able to withstand such pressures. In the absence of a larger collection of stories, we are unable to identify common approaches taken against suppression. It appears that a focus on scientific excellence and tenacity are two major factors likely to have contributed to the ability to withstand pressure. We encourage others to tell their stories. Bringing examples of these instances to attention will make them familiar enough to be less intimidating should others experience anything similar. Additional documented experiences will expand the base of stories and thus help colleagues to withstand the pressures wielded by special interests. Shining a light on these pressures will remove barriers, not only to advancing the science, but also to protecting the public interest.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.039 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".