'Public' Mediations in Public Parks: Equity, Planning and the Regulation of Behaviours
Bibliographic record
Abstract
This Major Research Paper examines how ideologies of nature are manipulated by local civic actors to regulate people’s behaviour in public parks and thereby plan specific demographics of people out of these spaces. Focusing on behaviours of cruising and loitering, I explore how legal, design, and urban planning tools are leveraged to control and criminalize these behaviours in two GTHA public parks: Marie Curtis Park in Toronto, and Gore Park in Hamilton. Methods of research include multiple site visits to each park, interviews with local stakeholders, as well as urban planning and mental health professionals, and a literature review. In researching the above, I address questions on how the identity of “public” is defined and constructed in public parks and argue that the current regulation of cruising and loitering in the above cases serve to constitute homophobic and classist notions of “the public”. This is a particularly pressing issue for urban planners as an increasing number of ailments within cities are linked to rising temperatures, poor air quality and psychological distress. Scholarly work has demonstrated a positive correlation between exposure to nature and the alleviation of the above conditions. As parks are a primary source of nature in urban areas, addressing how the regulation of behaviour in public parks can limit the access of certain demographics of people - particularly those that are already socially marginalized - to the health benefits provided by exposure to nature is an urgent social equity issue in today’s urban environment. The paper concludes by offering alternative models for planning urban public parks that allow for more equitable access to the health benefits provided by these spaces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.049 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".