The Food Sovereignty Landscape in the Coastal Region of Southcentral Alaska
Bibliographic record
Abstract
Results: Participants had a mean age of 51.4Æ6.6 years.They perceived healthy eating as: (1) having a balanced diet (e.g., incorporating all food groups in their meals), (2) preparing homemade meals, (3) eating foods low in fat, added sugar, and sodium, and (4) not having diseases.Individual-level facilitators included: (1) eating self-regulation, and (2) healthy food choices derived from their home-country.Interpersonal-level facilitators included: (1) wife/partner as a catalyst for healthy food choices, and (2) other family members social support for healthy food choices.Conclusions: Overweight or obese Central American men had a good understanding of what healthy eating was and linked healthy eating to absence of disease.Reducing food portion sizes and preferring to consume culturally traditional healthy foods in the U.S. were discussed as examples of individual-level facilitators of healthy eating.Wives/partners played a leading role in facilitating men's healthy food choices.Weight management interventions to promote healthy eating should capitalize on men's understanding of healthy eating.Interventions should also integrate spousal involvement given their perceived influence on men's food behaviors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".