Bibliographic record
Abstract
Warnings about the increased risk of contracting and suffering severe COVID-19 among fat people has been in the spotlight of public discourse and media attention since the pandemic began. Added to this has been widespread anxiety about the risks of weight gain that were predicted to follow public health restrictions that compelled Canadians to work and learn from home. Critical scholars assert that obesity and the problematization of the fat body are discursively constructed through the deployment of biopedagogies--instructive lessons about what it means to eat and live right. By framing and deploying lessons in right living, biopedagogies exert social control over individual and collective bodies, including by making the fat body problematic. In this article, the authors present a discourse analysis of the ways that Canadian news media have reported on the connection between COVID-19 and obesity and draws on biopedagogies as a theoretical framework to elucidate how fat phobia is promulgated by such reporting. The authors call for guidelines to curb fat phobic language and the scientifically inaccurate, discursive constructions of bodies, body weight, and health in news media.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| 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".