Where life and leisure intersect: exploring the outdoors as a site of contradictory experiences for person’s living in poverty
Bibliographic record
Abstract
For individuals experiencing poverty and homelessness, acting out their lives in public spaces can be complicated, as their very existence might be viewed as a transgression of a spaces’ conceptualization [Lefebvre, H. 1991. The Production of Space. London: Blackwell.]. Within this paper, through the work of Henri Lefebvre and Don Mitchell, I examine the ways in which representations of public outdoor spaces in cities impact the lived experiences of those who engage with the sites as a means of survival. Through this work, I argue that the right to be is reliant on an individual’s ability to acceptably (re)produce spaces as they were conceived, or to otherwise be forced to exist in marginal spaces [Mitchell, D., and N. Heynen. 2009. “The Geography of Survival and the Right to the City: Speculation on Surveillance, Legal Innovation, and the Criminalization of Intervention.” Urban Geography 30 (6): 611–632. doi:10.2747/0272-3638.30.6.611; Snow, D., and M. Mulcahy. 2001. “Space, Politics, and the Survival Strategies of the Homeless.” American Behavioral Scientist 45 (1): 149–169. doi:10.1177/00027640121956962]. The empirical insights in this work emerged from nine months of field work at Start Me Up Niagara, a community centre in St. Catharines, Ontario, Canada, which works with people experiencing poverty and homelessness.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.031 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".