The Politics of Policy Development to End Obesity for Aboriginal Youth in the Educational Environment
Bibliographic record
Abstract
Canada, a country of considerable wealth and resources, has one of the highest standards of living in the world. This country is politically organized as a democracy that is supportive of political and civil freedoms, yet inequalities among certain populations prevail. In general, Aboriginal people experience poorer economic, social, and environmental conditions than those of non-Aboriginal people (Canadian Population Health Initiative, 2005) and lower involvement in political and civil activity. This report also illustrates the inferior health status among Aboriginal people. Within the school system, an educational policy can serve to address an inequality. Hence, the purpose of the paper is to apply the tools outlined by Deborah Stone in her book, Policy Parodox: The Art of Political Decision Making (2002), to demonstrate why I believe school policies should be developed to prevent obesity among Aboriginal youth, to understand the politics of implementing these policies and to analyze and critique the ideas from hypothesized political opponents. Addressing these injustices provides recognition of the racism in present-day educational policy decision-making processes, which can result in more significant progress toward an equal and just society which ensures the health of Aboriginal peoples and successive generations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".