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Record W4281555430 · doi:10.3998/ptpbio.2629

When Virtues are Vices: 'Anti-Science' Epistemic Values in Environmental Politics

2022· article· en· W4281555430 on OpenAlexaboutno aff
Daniel Hicks

Bibliographic record

VenuePhilosophy Theory and Practice in Biology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsnot available
FundersUniversity of California, DavisUniversity of Texas at Dallas
KeywordsEpistemologyEpistemic virtueEpithetPhilosophy of scienceAppealPoliticsEpistemic communityVirtueSociologyAxiologyOrder (exchange)SkepticismPhilosophyPolitical scienceLawEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.351
Teacher spread0.314 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2022
Admission routes1
Has abstractyes

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