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Record W4410188744 · doi:10.1080/21550085.2025.2496835

Just Transition and Social Acceptability: A Canadian Case

2025· article· en· W4410188744 on OpenAlexafffundabout
Alexandre Gajevic Sayegh, Hubert Cadieux, Catherine Ouellet, Jeanne Desrosiers, Yannick Dufresne

Bibliographic record

VenueEthics Policy & Environment · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Education and Societal Dynamics
Canadian institutionsUniversité de MontréalUniversité Laval
FundersSocial Sciences and Humanities Research Council of CanadaFonds de recherche du Québec
KeywordsTransition (genetics)Political scienceBiology

Abstract

fetched live from OpenAlex

This paper takes place at the intersection of climate policy, public opinion and the ‘just transition’ debate. Its central hypothesis is: the inclusion of fairness measures in the green economy transition – especially by targeting workers in the most affected sectors – will have a positive impact on the social acceptability of climate policy. This paper focuses on two key policies: carbon pricing and a fossil fuel phase-out. A set of survey questions compare social support for these two policies (i) without and (ii) with accompanying fairness provisions, such as green jobs creation and support for workers. This paper uses an exclusive survey (n = 1,500) conducted in Canada in 2022. From the data, we observed that fairness provisions increase the support both for a higher price on carbon and for a decrease in the production of oil and gas, which was especially salient for groups initially less concerned by climate change.

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)0.009
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.764

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0410.025
Scholarly communication0.0090.003
Open science0.0020.008
Research integrity0.0050.007
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 head0.058
GPT teacher head0.389
Teacher spread0.331 · 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 designQualitative
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
Published2025
Admission routes3
Has abstractyes

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