A Complex Disease with Complex Discourse: Exploring the Online Messaging of Two Canadian Obesity Charities and the Implications for Weight Stigma
Bibliographic record
Abstract
Researchers warn that sizeism and weight stigma can prevent individuals from seeking health care, increase feelings of depression, and even contribute to weight gain and the worsening of negative health behaviours (Chrisler and Barney 2017; O’Hara and Gregg 2006; Puhl and Heuer 2009; 2010; Tomiyama 2014). The motivation for this study relates to a broader social problem of weight stigma and is premised upon evidence that suggests that stigmatizing content precipitates poor perceptions of obese individuals (Frederick et al. 2020; Puhl and Heuer 2010). Drawing upon the concept of biopedagogy, this case study qualitatively analyzes the online messages produced by two prominent obesity organizations in Canada (Harwood 2009; Rail 2012). Specifically, this study asks, how do obesity organizations frame and define obesity? How does the organizational context contribute to the framing of obesity-related messages? How do these organizations reproduce or challenge weight stigma? Multiple and competing message frames were observed, reflecting different paradigms of obesity discourse. In light of Obesity Canada’s goal to end weight bias, the framing of obesity as a disease is interpreted as an attempt to resolve the individual blame attached to obesity (Ata et al. 2018; Puhl and Heuer 2010). In contrast, the Childhood Obesity Foundation emphasizes parental responsibility and lifestyle change, upholding individualistic and oversimplistic explanations of obesity (O’Hara and Gregg 2006; Salas 2015). While this study draws upon past research about obesity discourse and weight stigma, it is the first of its kind to explore the online messaging of two Canadian obesity organizations.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.049 | 0.017 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".