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Record W4411370940 · doi:10.1038/s44168-025-00260-3

The current state of municipal climate action plans in effecting positive social justice outcomes in Canada

2025· article· en· W4411370940 on OpenAlexafffundabout
Nicola Radatus-Smith, Harshavardhan Jatkar, Garrett T. Morgan, Imre Szemán, Ian Hamilton

Bibliographic record

Venuenpj Climate Action · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsClimate justiceCurrent (fluid)State (computer science)Environmental justicePolitical scienceSocial justiceAction (physics)Economic JusticeEnvironmental planningCriminologyEnvironmental sciencePsychologyClimate changeComputer scienceLawEngineeringEcologyPhysicsBiology

Abstract

fetched live from OpenAlex

Abstract Climate change is disproportionately impacting marginalised communities and exacerbating social injustices in Canada. Municipal Climate Action Plans (CAPs) are beginning to look at the challenges of the climate crisis and social injustices by incorporating language related to decolonisation, equity, diversity, and inclusion (DEDI) in their recommended actions. However, how DEDI is incorporated into CAPs remains underexplored. Therefore, this review evaluates the CAPs of 20 cities in Canada for the ways in which DEDI concerns are incorporated in their plans by using a framework that assesses the plans’ development processes, collaboration with stakeholders, ownership of actions, and evaluation methods. Our analysis finds that, in general, Canadian municipal CAPs do not go far enough in addressing social injustices. The results indicate that there is an opportunity for Canadian cities to revise their existing CAPs to address the identified gaps during the implementation process in ways that maximise positive and just social outcomes.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.387
Teacher spread0.353 · 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 teacher head, 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

Citations1
Published2025
Admission routes3
Has abstractyes

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