Does Gender Have a Place in Greenspace Planning? Feminist Perspectives and the Toronto Ravine Strategy
Bibliographic record
Abstract
This paper addresses the intersection of gender, planning and greenspace by analyzing the Toronto Ravine Strategy and the planning process behind it. Through my investigation I conducted a literature review and interviewed participants of the Strategy to explore the research question, “Did gender play a role within the planning process of the Toronto Ravine Strategy?” I determine that it has not, proven by participants acknowledging a need to plan for difference, but not necessarily for gender. I explore the nature/culture dichotomy in greenspace that emerged from my research and how this could explain why participants were unwilling or unable to see the importance of considering gender. I introduce the connection between gender, greenspace and planning and define these key terms. I also include a review of literature on feminist political ecology, gender and planning/greenspace, women-friendly design features, how urban forests are political spaces, and explore the history of the Ravine Strategy. I reinforce the importance of the feminist methodologies/methods that informed my data collection and provide details about how I collected my data. My results review quotes from participants, as I connect my findings to the broader field of feminist literature. I conclude with future recommendations for the Toronto Ravine Strategy. Through my paper I address how we need to plan for difference and address this gap in how greenspaces are planned.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.029 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".