Portrayals of Canine Obesity in English-Language Newspapers and in Leading Veterinary Journals, 2000–2009: Implications for Animal Welfare Organizations and Veterinarians as Public Educators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In industrialized societies, more than 1 in 3 dogs and people currently qualify as overweight or obese. Experts in public health expect both these figures to rise. Although clinical treatment remains important, so are public perceptions and social norms. This article presents a thematic analysis of English-language mass media coverage on canine obesity from 2000 through 2009 and compares these results with a thematic analysis of articles on canine obesity in leading veterinary journals during the same time period. Drawing on Giddens's theory of structuration, this study identified articles that emphasized individual agency, environmental structure, or both as contributors to canine obesity. Comparisons with weight-related health problems in human populations were virtually absent from the veterinary sample. Although such comparisons were almost always present in the media sample, quotations from veterinarians and other spokespeople for the welfare of nonhuman animals emphasized the agency of individual caregivers (owners) over structural influences. Now that weight gain and obesity have been established as a pressing animal welfare problem, these results suggest a need for research and for interventions, such as media advocacy, that emphasize intersections between animal-owner agency, socioenvironmental determinants, and connections between animal welfare and human health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it