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
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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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".