Defending scientific integrity in conservation policy processes: lessons from Canada, Australia, and the United States
Bibliographic record
Abstract
Government agencies faced with politically controversial decisions often discount or ignore scientific information, whether from agency staff or non-governmental scientists. Recent developments in scientific integrity (the ability to perform, use, communicate and publish science free from censorship or political interference) in Canada, Australia and the United States demonstrate a similar trajectory: a perceived increase in scientific integrity abuses is followed by concerted pressure by the scientific community, leading to efforts to improve scientific integrity protections under a new administration. However, protections are often inconsistently applied, and are at risk of reversal under administrations that are publicly hostile to evidence-based policy. We compare recent challenges to scientific integrity to determine what aspects of scientific input into conservation policy are most at risk of political distortion and what can be done to strengthen safeguards against such abuses. To ensure the integrity of outbound communication from government scientists to public, we suggest that governments strengthen scientific integrity policies, include scientists’ right to speak freely in collective bargaining agreements, guarantee public access to scientific information, and strengthen agency culture supporting scientific integrity. To ensure the transparency and integrity with which information from non-governmental scientists (e.g., submitted comments or formal policy reviews) informs the policy process, we suggest that governments broaden the scope of independent reviews, ensure greater diversity of expert input with transparency regarding conflicts of interest, require substantive response to input from agencies, and engage proactively with scientific societies. For their part, scientists and scientific societies have a civic responsibility to engage with the wider public to affirm that science is a crucial resource for developing evidence-based policy and regulations that are in 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 | 0.030 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.026 | 0.019 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".