Gender and Climate Justice in Canada: Stories from the Grassroots
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.078 | 0.020 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".