Fizzy foibles: examining attitudes toward sugar-sweetened beverages in Michigan
Bibliographic record
Abstract
Health educators are increasingly publicizing the noxious health effects of sugar-sweetened beverages (SSB). American counties are implementing SSB taxes, as modeled after the ‘success’ of tobacco policies, to address the ‘obesity crisis’. Under-explored is how the linking of SSB to a stigmatized condition, ‘obesity’, has affected attitudes toward the purchasing and consumption of SSB and those individuals who purchase and consume SSB. This study sought to explore individuals’ attitudes and experiences with SSB in rural Michigan. Three themes emerged: Negativity, Egregious beverages and Implicated imbibers. These attitudes were situated within a context of increased exposure to SSB health discourses, particularly among younger participants. Additional themes arose regarding which SSB were labelled especially problematic, who should or should not be drinking SSB, as well as the consequences of drinking SSB. Pop/soda, energy drinks, and diet pop/soda were identified as particularly harmful, albeit for different reasons – sugar, caffeine, and unnatural additives. Many participants reported reacting negatively when they saw children of higher weights drinking SSB, though judgement was reserved for parents. Ultimately, SSB and their consumers appear increasingly stigmatized in ways that carry important equity implications for already marginalized groups.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".