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Record W4365484903 · doi:10.1002/ajcp.12675

Closing the equity deficit: Sustainability justice in municipal climate action planning in Waterloo region

2023· article· en· W4365484903 on OpenAlexafffund
Jennifer Dobai, Manuel Riemer

Bibliographic record

VenueAmerican Journal of Community Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEquity (law)Environmental justicePublic relationsClimate justiceSociologySustainabilityClimate changePolitical scienceEnvironmental resource managementEconomicsLawEcology

Abstract

fetched live from OpenAlex

There is growing recognition that often well-intended climate action solutions perpetuate and exacerbate manifestations of colonialism and racism due to the lack of equity and justice considerations in designing and implementing these solutions. There is limited research exploring why the integration of these considerations are lacking in municipal climate action planning. This exploratory descriptive qualitative study explored how municipal actors perceive and understand equity and justice in municipal climate action planning as a step toward addressing this issue. Semistructured interviews were conducted with seven members of the core management group from ClimateAction Waterloo region, and a template analysis of the interview data resulted in six themes. Findings suggested that those involved in municipal climate action planning understand and perceive justice and equity considerations as important to their work, however, translating this understanding to practice is a challenge due to structural (governmental and societal) and capacity (limited time, funding, resources, and knowledge) barriers. By better understanding how key actors consider justice and equity, we identify shifting colonial mental models as a potential pathway for transformative change given the central role of these actors.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.330
GPT teacher head0.502
Teacher spread0.172 · 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 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

Citations9
Published2023
Admission routes2
Has abstractyes

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