When Virtues are Vices: 'Anti-Science' Epistemic Values in Environmental Politics
Bibliographic record
Abstract
Since at least the mid-2000s, political commentators, environmental advocates, and scientists have raised concerns about an “anti-science” approach to environmental policymaking in conservative governments in the US and Canada. This paper explores and resolves a paradox surrounding at least some uses of the “anti-science” epithet. I examine two cases of such “anti-science” environmental policy, both of which involve appeals to epistemic values that are widely endorsed by both scientists and philosophers of science. It seems paradoxical to call an appeal to epistemic values “anti-science.” I develop an analysis that, I argue, can resolve this paradox. This analysis is a version of the “aims approach” to science and values, drawing on ideas from axiology and virtue ethics. I characterize the paradox in terms of conflicts or tensions between epistemic and pragmatic aims, and argue that there is a key asymmetry between them: epistemic aims are valuable, in part, because they are useful for pursuing pragmatic aims. Thus, when epistemic and pragmatic aims conflict, epistemic aims need to be reconceptualized in order to reconcile them to pragmatic aims. When this is done, in the “anti-science” cases, the epistemic values are scientific vices rather than virtues. Thus the “anti-science” epithet is apt.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.087 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| 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".