The Body Image Project: Mobilizing Policy Research for Children’s Health
Bibliographic record
Abstract
Knowledge mobilization projects attempt to make theory and research available in order to inform policy and practice. This paper describes a knowledge mobilization project at a Canadian university. A database of Canadian health curriculum policies was analyzed to discern the general approaches to body image across the country. The findings show that learning how to cultivate a positive body image is inconsistently addressed across the education policies of the thirteen provinces and territories. Secondly, many Canadian curriculum policy documents have missed opportunities to teach acceptance of diverse body types and other protective factors. Third, health is more strongly associated with fitness in policies than with more holistic approaches. A knowledge mobilization website project was established to encourage more critical understandings of healthy self-esteem and body image. The website contains summaries of current research pertaining to body image, child and adolescent development, and key messages about body-positive health. The online and open source material available includes ageappropriate lessons for teachers and parents. These materials have been designed to translate research into activities, lessons, and key messages that promote healthy body image and self-esteem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".