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Record W4417243695 · doi:10.3138/jcs-2025-0008

Two Tales of Climate Politics in Canada: How Notions of Canadian Cultural Identity and Common-Sense Shape Canadian Climate Coalitions

2025· article· en· W4417243695 on OpenAlexaffvenueabout
Michael Dodich, Jesse Salah Ovadia

Bibliographic record

VenueJournal of Canadian Studies · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicSustainability and Climate Change Governance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHegemonyPoliticsChampionNeoliberalism (international relations)Climate justiceIdentity politicsIdentity (music)Climate change

Abstract

fetched live from OpenAlex

This article explores the hegemonic role of oil and gas in Canadian politics and culture alongside contestation over renewable energy (RE). Federally, the governing Liberals represent a middle-ground coalition that both supports the oil/gas industry and seeks to portray itself as a champion of climate action. Significant progress on climate goals is undermined by actions that lock in large-scale carbon emissions for decades. The Ontario Liberal Party phased out coal and invested heavily in wind energy. However, after 2018, the Conservatives have steered Ontario away from wind. The authors argue that differing climate coalitions have produced different notions of national identity and common sense when it comes to oil/gas and RE. At both the federal and provincial levels, culture is being mobilized successfully by opponents of climate action against green energy transitions. Countering the hegemony of neoliberal extractivism requires a transformation of Canadian identity vis-à-vis oil and gas and an embrace of the common sense of RE.

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)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.906

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0530.024
Scholarly communication0.0140.004
Open science0.0020.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.288
Teacher spread0.251 · 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 designNot applicable
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

Citations0
Published2025
Admission routes3
Has abstractyes

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