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Record W3083883287

Gender and Climate Justice in Canada: Stories from the Grassroots

2017· article· en· W3083883287 on OpenAlexaboutno aff
Patrícia E. Perkins

Bibliographic record

VenueYork University Digital Library (York University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsClimate justicePolitical scienceEconomic JusticeEnvironmental justiceSocial justiceClimate changeSociologyGender studiesCriminologyLawPolitics
DOInot available

Abstract

fetched live from OpenAlex

Climate change has gendered effects across Canada. Extreme weather events, warming cities, melting sea ice and permafrost, ice storms, floods, droughts, and fires related to climate change are directly and indirectly causing widespread economic and social impacts. Fossil fuel extraction, transport, and processing affect many people in Canada. Women and men have different experiences and views regarding climate change, and are affected differently as a function of their gendered social and economic positions. They also have different access to redress and to policy processes shaping public responses. Indigenous women, in particular, are on the front lines of climate injustice and are leading inspiring resistance movements. This paper examines climate justice issues across Canada through a gender lens,
\nusing a literature review and interviews with researchers and activists to identify the major themes and knowledge gaps. The paper also summarizes preliminary results of grassroots research into how individuals, community-based organizations, women’s groups and indigenous activists across Canada experience and articulate the gendered impacts of climate change, what their priorities are for action, and how they are organizing -- for example, by incorporating climate change education, outreach, networking, activism, and policy development into their work.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.004
Open science0.0010.000
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 head0.069
GPT teacher head0.218
Teacher spread0.148 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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