Gender Justice and Climate Justice: Community-Based Strategies to Increase Women's Political Agency in Watershed Management in Times of Climate Change
Bibliographic record
Abstract
Socially vulnerable people, and women in particular, are disproportionately affected by global climate change because of their gendered socioeconomic roles and often their geographic location; yet they are least equipped to deal with those impacts due to their disadvantaged economic and political position. Women, however, have special contributions to make towards climate change adaptation because of gendered differences in positional knowledge of ecological and water-related conditions. To date, women have been largely underrepresented, and in the majority of cases, excluded from formal decision-making processes related to climate change mitigation and adaptation. Including women in these processes and building their capacity and resilience is required for the development of effective and gender-sensitive climate change adaptation policy. Also, preparing women for the short and long-term impacts of climate change is crucial for addressing some of the social aspects of this phenomenon and for preventing further aggravation of existing gender inequalities. This paper discusses South-North initiatives and models for community-based environmental and climate change education which are using the democratic opening provided by watershed-based governance structures to broaden grassroots participation, especially of women, in political processes. We outline the activities and results of two international projects, the Sister Watersheds project, with Brazilian and Canadian partners (2002-2008), and a Climate Change Adaptation in Africa project with partners in Canada, Kenya, Mozambique, and South Africa (2010-2013).
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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.
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".