‘Active play may be lots of fun, but it's certainly not frivolous’: the emergence of active play as a health practice in Canadian public health
Bibliographic record
Abstract
In the context of what has been termed a childhood obesity epidemic, public health institutions have recently begun to promote active play as a means of addressing childhood obesity, thus advancing play for health. Drawing on Foucault, this article problematises the way that children's play is being taken up as a health practice and further considers some of the effects this may have for children. Six Canadian public health websites were examined, from which 150 documents addressing children's health, physical activity, obesity, leisure activities and play were selected and coded deductively (theoretical themes) and inductively (emerging themes). Bacchi's () question-posing approach to critical discourse analysis deepened our analysis of dominant narratives. Our findings suggest that several taken-for-granted assumptions and practices underlie this discourse: (i) play is viewed as a productive activity legitimises it as a health practice; (ii) tropes of 'fun' and 'pleasure' are drawn on to promote physical activity; (iii) children are encouraged to self-govern their leisure time to promote health. We underscore the need to recognise this discourse as contingent and as only one of many ways of conceptualising children's leisure activities and their health and social lives more generally.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.039 | 0.068 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".