Planting seeds of change: reconceptualizing what people eat as eating practices and patterns
Bibliographic record
Abstract
Language focused on individual dietary behaviors, or alternatively, lifestyle choices or decisions, suggests that what people eat and drink is primarily a choice that comes down to free will. Referring to and intervening upon food consumption as though it were a freely chosen behavior has an inherently logical appeal due to its simplicity and easily defined targets of intervention. However, despite decades of behavioral interventions, population-level patterns of food consumption remain suboptimal. This debate paper interrogates the manner in which language frames how problems related to poor diet quality are understood and addressed within society. We argue that referring to food consumption as a behavior conveys the idea that it is primarily a freely chosen act that can be ameliorated through imploring and educating individuals to make better selections. Leveraging practice theory, we subsequently propose that using the alternative language of eating practices and patterns better conveys the socially situated nature of food consumption. This language may therefore point to novel avenues for intervention beyond educating and motivating individuals to eat more healthfully, to instead focus on creating supportive contexts that enable sustained positive dietary change. Clearly, shifting discourse will not on its own transform the science and practice of nutrition. Nevertheless, the seeds of change may lie in aligning our terminology, and thus, our framing, with desired solutions.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.108 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.011 |
| 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".