Bibliographic record
Abstract
In this article, I begin to unpack how urban physical activity space is being imagined by physical activity policy-makers. I review literature pertaining to youth, urban space and play, and I engage in a preliminary analysis of a small selection of government (Canada) and media communications to examine how space and health are represented discursively in policy texts. These representations of space and health are not necessarily new, but are appearing to become more prevalent in Canadian public health contexts as the `panic' about youth crime and physical (in)activity escalates. I am concerned with how space is represented in policy texts and what space is called onto be(come) in the service of the new public health in Toronto, Canada. I examine how youth are discursively incited, and in what ways, to make use of, and to find salvation in, `healthified' urban spaces. In current neoliberal and neo-conservative times, youth are increasingly called upon to engage in healthy living in spaces that are replete with discourses of `healthification', civic engagement and consumerism. I conclude by suggesting that we need to pay attention to current investments in urban youth's active living space, how urban youth take up and/or refuse spatial inscriptions and prescriptions, and how youth imagine themselves as subjects of healthified urban spaces (that are best thought of in terms of a complicated network of hegemonic local and global interrelations).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.096 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".