‘You have to do 60 minutes of physical activity per day … I saw it on <scp>TV</scp>’: Children's constructions of play in the context of Canadian public health discourse of playing for health.
Bibliographic record
Abstract
Public health institutions in many industrialised countries have been launching calls to address childhood obesity. As part of these efforts, Canadian physical activity campaigns have recently introduced children's play as a critical component of obesity prevention strategies. We consider this approach problematic as it may reshape the meanings and affective experiences of play for children. Drawing on the analytical concept of biopedagogies, we place Canadian public health discourse on play in dialogue with children's constructions of play to examine first, how play is promoted within obesity prevention strategies and second, whether children take up this public health discourse. Our findings suggest that: (i) the public health discourse on active play is taken up and reproduced by some children. However, for other children sedentary play is important for their social and emotional wellbeing; (ii) while active play is deemed to be a solution to the risk of obesity, it also embodies contradictions over risk in play, which children have to negotiate. We argue that the active play discourse, which valorises some representations of play (that is, active) while obscuring others (that is, sedentary), is reshaping meanings of play for children, and that this may have unintended consequences for children's wellbeing.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.029 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".