“Coke’s not a food”: A critical discourse analysis of sugar-sweetened beverage tax acceptability by white residents from an upper-middle class neighborhood in Winnipeg Manitoba
Bibliographic record
Abstract
Increasing concerns about the health impacts of sugar consumption has led to the proposition of a sugar-sweetened beverage (SSB) tax in Canada. However, competing concerns related to stigma and equity remain and have not been explored in a Canadian context. As part of a broader study examining the perspectives of various populations on SSB tax acceptability, we examined how residents of an upper-middle class neighborhood conceptualize SSB tax acceptability, and we explored the discourses that inform their discussion. We conducted and analyzed qualitative, semi-structured interviews with residents of an upper-middle class neighborhood in Winnipeg, Manitoba, Canada. Recruitment criteria were residence, adults, and English speaking. Critical discourse analysis methodology was used, and healthism (health moralism) and tax psychology informed the analysis. Eighteen participants volunteered: 15 females and 3 males; all self-identified as white, and all spoke about (grand)parenting. Healthist discourse was utilized in supportive discussion of SSB taxation. With the mobilization of healthism, ideal citizens and parents were described as "health conscious" and those who might be likely to reduce SSB intake because of taxation. Healthism also contributed to their identification of beverages targeted by a tax, versus those they deemed as having redeeming nutritional qualities. Limits to SSB tax support were expressed as fairness concerns, with a focus on the procedural justice of the tax. Participants supported SSB taxation and the discourses they employed suggested support for the tax was perceived as contributing to their construction of the kind of ideal, health-valuing citizens they hoped to embody. However, participants were also concerned about the fairness of implementation, although this did not outweigh the prioritization of good 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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.031 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".