Science and Environmental Policy‐Making: Bias‐Proofing the Assessment Process
Bibliographic record
Abstract
Scientific assessment panels are playing increasingly influential roles in national and international policy formation. Although they typically appeal to the standard of journal peer review as their quality control criterion, there seems to be confusion about what peer review actually does. It is, at best, a necessary condition of reliability, but not a sufficient condition. There is also the problem that assessment panels may be biased in favor of one side or another when evaluating areas in which the science is unclear. In this paper I argue that additional checks and balances are needed on the information going into scientific assessment reports when it will be used to justify major policy investments. I propose two new mechanisms to bias‐proof the outcome: an Audit Panel and a Counterweight Panel. The need for such mechanisms is discussed with reference to the “hockey stick” debate in climate change.
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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 | 0.567 | 0.737 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".