Concerning the politicization of climate science: epistemic dependency, trust in expert testimony, and determining What We Ought to Believe
Bibliographic record
Abstract
Belief in climate change does not divide into a simple dichotomy of (good) believers and (evil) \nnonbelievers. An unclear view of skepticism arises when the differences between empirical and \nnormative claims are revealed. Developing responsible beliefs on matters of which we possess \nno expertise requires reliable expert testimony. However, trust and objectivity are integral factors \nfor belief in expert consensus. A reduction in public opinion regarding the reliability of climate \nscience, due to politicization, enables the dismissal anthropogenic climate change. \nUnderstanding politicization from both Pielke and Douglas clarifies a negative role that politics \ncan play in the doing of science. The risks that politicization pose, mistrust for one, do not \nundermine the necessary role of values in science. The role of values within scientific enquiry \nmust be restricted and acknowledged for trustworthy science to be produced, and for scientific \nfindings regarding climate change to be accepted by nonexperts, including policymakers.
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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 | 0.025 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".