Bibliographic record
Abstract
Despite sharing common interests in being advocates for social change, feminist and environmental geographers have yet to acknowledge interests they share in common. Environmental geographers, particularly those focused on policy and institutional analysis, have not embraced feminist theories or methodologies, while few feminist geographers have engaged issues associated with environmental policy‐making. Our purpose is to initiate a dialogue about how linkages might be forged between feminist and environmental geography, particularly among Canadian environmental geographers working on institutional and policy analysis. We begin by illustrating that environmental geographers working on Canadian problems have neglected to introduce gender as an analytical category or feminist conceptual frameworks to guide their research. Second, we identify four feminist research approaches that should also be pursued in environmental geography. Third, we consider examples of how feminist perspectives might be incorporated in three themes of environmental geography: institutional and policy analysis, participatory environmental and management systems and alternative knowledge systems. Fourth, we consider two research frameworks—political ecology and environmental justice—and suggest that these may be useful starting points for integrating feminist analysis into environmental geography. Last, we summarise our suggestions for how future research of feminist and environmental geographers could benefit from a closer association.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.040 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".