MétaCan
Menu
Back to cohort
Record W3006855376 · doi:10.1002/wcc.640

Just urban transitions: Toward a research agenda

2020· article· en· W3006855376 on OpenAlexaff
Sara Hughes, Matthew J. Hoffmann

Bibliographic record

VenueWiley Interdisciplinary Reviews Climate Change · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScholarshipClimate justiceClimate changeEconomic JusticeAction (physics)Work (physics)Political economy of climate changePolitical scienceClimate change mitigationEnvironmental justiceOrder (exchange)Environmental ethicsSociologyLawEcologyBusiness

Abstract

fetched live from OpenAlex

Abstract While there are excellent policy and academic foundations for thinking about and making sense of urban climate action and questions of justice and climate change independently, there is less work that considers their intersection. The nature and dynamics of, and requirements for, a just urban transition (JUT)—the fusion of climate action and justice concerns at the urban scale—are not well understood. In this review article we seek to rectify this by first examining the different strains of justice scholarship (environmental, energy, climate, urban) that are informing and should inform JUT. We then turn to a discussion of just transitions in general, tracing the history of the term and current understandings in the literature. These two explorations provide a foundation for considering both scholarly and policy‐relevant JUT agendas. We identify what is still needed to know in order to recognize, study, and foster JUT. This article is categorized under: The Carbon Economy and Climate Mitigation > Benefits of Mitigation Climate, Nature, and Ethics > Climate Change and Global Justice

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)0.020
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.006
Science and technology studies0.0050.023
Scholarly communication0.0140.037
Open science0.0030.013
Research integrity0.0110.015
Insufficient payload (model declined to judge)0.0100.001

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 head0.479
GPT teacher head0.486
Teacher spread0.008 · 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 designTheoretical or conceptual
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

Citations166
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueWiley Interdisciplinary Reviews Climate ChangeSame topicEnvironmental Justice and Health DisparitiesFrench-language works237,207