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Record W1732349874 · doi:10.1111/gove.12003

Valence, Policy Ideas, and the Rise of Sustainability

2012· article· en· W1732349874 on OpenAlexafffund
Robert Henry Cox, Daniel Béland

Bibliographic record

VenueGovernance · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Policy and Administration Research
Canadian institutionsUniversity of Saskatchewan
FundersCanada Research Chairs
KeywordsValence (chemistry)SustainabilityCognitive reframingEconomicsRealmPublic policyPublic economicsPolitical scienceEconomic growthPsychologySocial psychologyLawPhysics

Abstract

fetched live from OpenAlex

This article introduces to policy studies the concept of valence, which we define as the emotional quality of an idea that makes it more or less attractive. We argue that valence explains why some ideas are more successful than others, sometimes gaining paradigmatic status. A policy idea is attractive when its valence matches the mood of a target population. Skilled policy entrepreneurs use ideas with high valence to frame policy issues and generate support for their policy proposals. The usefulness of the concept of valence is illustrated with the case of sustainability, an idea that has expanded from the realm of environmental policy to dominate discussions in such diverse policy areas as pension reform, public finance, labor markets, and energy security. As the valence of sustainability has increased, policy entrepreneurs have used the idea to reframe problems in these various policy areas and promote reforms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0050.032
Scholarly communication0.0150.009
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.018
GPT teacher head0.383
Teacher spread0.365 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations158
Published2012
Admission routes2
Has abstractyes

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