Bibliographic record
Abstract
The newly emerging field of positive psychology focuses on the positive facets of life, including happiness, life satisfaction, personal strengths, and flourishing. Research in this field has empirically identified many important benefits of enhanced well-being, including improvements in blood pressure, immune competence, longevity, career success, and satisfaction with personal relationships. Recognizing these benefits has motivated researchers to identify the correlates and causes of well-being to inform them in the development and testing of strategies and interventions to elevate well-being. As positive psychology researchers throughout the world have turned their attention toward facets of food intake, a consensus is developing that the consumption of healthy foods can enhance well-being in a dose-response fashion. The link between unhealthy foods and well-being is less clear. Some studies suggest that under certain conditions, fast food may increase happiness, though other studies demonstrate that fast food can indirectly undermine happiness. The positive impact of food consumption on well-being is not limited to what people consume but extends to how they consume it and social factors related to eating. Though the research suggests that our food intake, particularly fruits and vegetables, increases our well-being, this research is in its infancy. Research specifically focused on subpopulations, including infants and pregnant mothers, is mostly lacking, and the mechanisms that underlie the relationship between food consumption and well-being remain to be elucidated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".