MétaCan
Menu
Back to cohort

How Do We Judge Policies?

2014· article· en· W2008607371 on OpenAlex
Richard D. French

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Political Quarterly · 2014
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsGlobal Affairs Canada
Fundersnot available
KeywordsJudgementPoliticsPsychologyWork (physics)Social psychologyEpistemologySociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Political science has paid scant attention to the way that citizens judge public policy, assuming that citizens do so, or should do so, in ways familiar to academics themselves, depending upon which of the various schools of thought they endorse. This paper argues that approaching citizens’ judgement realistically requires attention to political psychology. Indeed, our conception of citizen judgement can be enriched by attention to research and theory in cognitive psychology and neuroscience. That work emphasises that much judgement occurs spontaneously and very rapidly, that it is involuntary and non‐semantic and that it depends upon the emotional impact of experience rather than conscious weighing of situations against explicit standards of assessment such as science, self‐interest or moral theory. A moral psychology for public life is sketched out, with implications for judgment by politicians.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.533
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.075
GPT teacher head0.288
Teacher spread0.212 · 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