“If more people cut out gluten, the zombies would wake up”: the construction of health-related concerns by gluten-free food consumers
Bibliographic record
Abstract
Purpose Markets for free from foods have undergone extensive growth as consumers attempt to manage their health in increasingly novel ways. This research explores the making of consumer perceptions about the health of gluten-free foods. Design/methodology/approach This research employs qualitative methods including in-depth interviews with consumers of gluten-free foods and content analysis of online consumer comments. Findings Findings illustrate how consumers leverage personal responsibility, social commentary and political criticism in ways that forge essential connections with traditional medical authority. In particular, consumers blend diverse views together by expressing reverence, positioning complementarity and framing temporality. Research limitations/implications This research highlights the productive role of consumers in shaping what constitutes health-related concerns and widens the scope of explanatory factors beyond product- and individual-level differences. This research is set in the context of gluten-free foods and draws on interview data from a single set of consumers. Future research could consider other free from markets including, for example, soy-free foods and corn-free foods, both of which implicate some of the most common ingredients in food products and potential regional differences both within and outside of North America. Practical implications This research offers insights into the marketing of gluten-free foods and free from foods in general, specifically the participation of consumers in legitimising the need for these foods on the basis of health. Originality/value I weave together multiple streams of work across disciplines including food marketing, contested illnesses and institutional logics to further our understanding of the dynamic nature of contemporary markets for free from foods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".