An Exploration of Discursive Framings of Body Weight in <i>Obesity</i> <i>Canada</i> and the <i>Association for Size Diversity and Health</i> ’s Websites
Bibliographic record
Abstract
Body weight discourse varies based on which researcher you speak to. This can be confusing for those new to the field of weight science, especially for students or early career researchers. Organizations like Obesity Canada have student-led “Student and New Professional” groups which discuss higher-weights as “obesity” with students in healthcare related fields. Medicalizing body weight and labelling obesity as a chronic disease differs from Health At Every Size® or the work that fat studies scholars are doing. This can create turmoil for students, unsure of where they fit in these discussions of weight. This research paper was completed as part of an undergraduate directed study to explore discourse as a method to analyze body weight paradigms. First, discourse is explored as a method, then the medicalization of body weight is questioned. Viewpoints like those of Obesity Canada, Health At Every Size®, and fat studies scholars are explored and compared. A discourse analysis was completed to compare and contrast Obesity Canada and the Association of Size Diversity and Health ’s websites. Findings indicate fatness can be framed differently, dependant on positionality (e.g., healthist or sizeist) and ideologies present on online websites.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.029 | 0.040 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".