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Record W7084285807

Feminist Climate Policy in Industrialised States

2025· other· en· W7084285807 on OpenAlexfundno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsnot available
FundersInternational Council for the Exploration of the SeaNordForskSvenska Forskningsrådet FormasQueen's UniversitySwansea UniversityDurham UniversityEuropean CommissionEnergimyndighetenUdenrigsministerietUniversity of LeedsUniversity of OxfordVetenskapsrådetGovernment of Canada
KeywordsTransformative learningClimate justiceClimate changeGender mainstreamingPoliticsPraxisPolitical economy of climate changeUnderpinningClimate governance
DOInot available

Abstract

fetched live from OpenAlex

Feminist Climate Policy in Industrialised States explores ways in which policymakers can overcome institutional barriers and conventions in pursuit of the radical changes necessary for a gender-just climate emergency response. In 2021, the Intergovernmental Panel on Climate Change acknowledged that addressing the climate emergency must involve social justice and equality. Feminist approaches to decision-making, policy-making, community organising and their underpinning methodologies can enable this. The authors draw critically on case studies, research and interviews with feminist practitioners, legislators and leaders who have implemented significant changes, to signal how change might be achieved and ask what lessons can be drawn. The book posits that we need to ultimately move beyond the gender mainstreaming and gender equality issues which have been integrated into existing – and failing – structures, to more transformative feminist approaches. It concludes by identifying key strands of feminist-oriented praxis that offer the potential to expedite responses to climate change across multiple levels of governance. With industrialised states shifting rightwards to a politics which diminishes the importance and urgency of gender equality, diversity, human rights and the need for climate action, this volume will inspire, guide, and provide tools for policymakers, politicians, community activists, academics, and students to take transformative action to address the climate emergency. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons [Attribution-Non Commercial-No Derivatives (CC BY-NC-ND)] 4.0 license.

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)0.002
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.009
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.254
Teacher spread0.235 · 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
GenreOther

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 routes1
Has abstractyes

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