Geographic and Longitudinal Trends in Media Framing of Obesity in the United States
Bibliographic record
Abstract
OBJECTIVE: The media's framing of public health issues is closely linked to public opinion on these issues and support for interventions to address them. This study characterized geographic and temporal variation in the US media's framing of obesity across states from 2006 to 2015. METHODS: Newspaper articles that mentioned the term obesity were drawn from Access World News (NewsBank, Inc., Naples, Florida), a comprehensive online database (N = 364,288). This study employed automated content analysis, a machine learning technique, to categorize articles as (1) attributing obesity to individual-level causes (e.g., lifestyle behaviors), (2) attributing obesity to environmental/systemic causes (e.g., neighborhood walkability), (3) attributing obesity to both individual-level causes and environmental/systemic causes, or (4) articles without any such attribution framework. RESULTS: Nationwide across all years, a higher proportion of articles focused on individual-level attribution of obesity than environmental-level attribution or both. Missouri and Idaho had the highest proportions of articles with an individual framework, and Nevada, Arkansas, and Wisconsin had the highest proportions of articles with an environmental framework. CONCLUSIONS: This analysis demonstrates that US media sources heavily focus on an individual framing of obesity, which may be informing public perceptions of obesity. By highlighting differences in obesity media portrayal, this study could inform research to understand why particular states represent outliers and how this may affect obesity policy making.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".