Go the whole nine yards? How extent of meat restriction impacts individual dietary experience
Bibliographic record
Abstract
There are a variety of approaches to addressing meat overconsumption including forms of meat restriction that vary by the degree of reductions and the type of meat reduced. This study examines three such diets—a vegetarian diet, a reduced-meat diet, and a chicken-free diet—with a focus on the differences in the lived dietary experiences of their adherents. These lived experiences are operationalized using a variety of measures: satisfaction with food-related life, social ties, convenience, social/personal life, health, cost, motivation, identity, perception of prevalence rates, length of diet adherence, and the theory of planned behavior (intentions, attitudes, perceived behavioral control, and subjective norms). The data comes from an online survey of a cross-sectional, census-balanced sample of more than 30,0000 U.S. residents aged 18+ years sourced from Nielsen’s Harris Panel. The results showed meat reducers to be a larger group than previously suspected, with a third of American adults self-identifying as reducing their meat consumption, compared to one percent each who identify as a vegetarian or chicken avoider. The findings also demonstrated that a vegetarian diet had the strongest lived dietary experiences among American adults who are currently eating one of the meat-restricted diets. This research speaks to how the degree and type of meat restriction can impact an individual’s lived experience with their diet.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".