Designing for Safety: A Feminist Approach to Campus Planning at Queen’s University
Bibliographic record
Abstract
Women-identifying students are disproportionately impacted by harassment and assault in post-secondary settings, leading to heightened fear in public campus spaces. Safety concerns result in precautionary behaviours that limit their mobility and full participation in campus life, contributing to social and academic inequities compared to men-identifying students. This report examines Queen’s University’s Campus Master Plan and the built form of the Main Campus by identifying factors influencing women-identifying students’ perceptions of safety. The objectives of this research are to: 1) Identify the spatial components of Queen’s University’s Main Campus, particularly Snodgrass Arboretum, that influence women-identifying students’ perceptions of safety 2) Assess Queen’s University’s Campus Master Plan to determine its efforts in promoting campus safety 3) Offer recommendations to improve the built environment at Queen’s University and other institutions of higher education, creating a safer campus experience for women-identifying students. To achieve these objectives, several research methods were employed. An adapted version of METRAC’s Campus Safety Audit Guide was used to assess Snodgrass Arboretum—one of the campus’s largest green spaces. The audit, rooted in CPTED and PAR, allowed participants to serve as experts of experience, identifying factors within the site that made them feel safe and unsafe. A focus group debriefing session and mapping exercise supported audit findings and allowed for further discussion of emerging themes across the Main Campus. A document review of Queen’s Campus Master Plan examined the presence and quality of campus safety policies. Findings indicate opportunities to enhance perceptions of safety on campus through both design and policy interventions. Design-wise, improved visibility, the animation of spaces to encourage bystander presence, and increased maintenance of safety features emerged as prominent themes. Policy findings emphasized the need for stronger monitoring to ensure implementation, better advertising of safety guidelines, and the inclusion of a dedicated safety section within the Campus Master Plan. Eleven recommendations were developed not only to make Queen’s a safer campus but also to establish a benchmark for advancing campus safety at similar Canadian institutions. Violence against women is a systemic issue that cannot be solved by design or policy alone—but integrating these tools is a necessary step toward creating safer, more inclusive spaces for girls and women within our communities.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".