“Vulnerable,” “At-risk,” “Disadvantaged”: How <i>A Framework</i> <i>for Recreation in Canada 2015: Pathways to Wellbeing</i> Reinscribes Exclusion
Bibliographic record
Abstract
The purpose of this paper is to analyze how the problem of exclusion and the solution of inclusion have been discursively produced within A Framework for Recreation in Canada 2015: Pathways to Wellbeing.Centred around three main arguments, our analysis demonstrates the ways in which the Framework strategically combines notions of Otherness with discourses of risk and inclusion in order to target social groups that are perceived to be lacking the personal, cultural, or material resources to participate in the specified amount of physical activity required for health (and thus national economic) benefits. By interrogating the ways the Framework has enacted a particular Canadian ‘brand(ing)’ of inclusion, our analysis challenges recreation professionals (including academics) to think outside the inclusion/exclusion binary and consider their complicity in the creation, and maintenance, of exclusionary practices.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.049 |
| Scholarly communication | 0.019 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| 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".