Well-Being Science for Teaching and the General Public
Bibliographic record
Abstract
Research on well-being has exploded in recent years to more than 55,000 relevant publications annually, making it difficult for psychologists-including key communicators such as textbook authors-to stay current with this field. Moreover, well-being is a daily concern among policymakers and members of the general public. Well-being science is relevant to the lives of students-illustrating the diverse methods used in the behavioral sciences, presenting highly replicated findings, and demonstrating the diversity of individuals and cultures. Therefore, in this article, we present eight major findings that teachers and authors should seriously consider in their coverage of this field. These topics include processes such as adaptation, influences such as income, the benefits of well-being, and cultural and societal diversity in well-being and its causes. We also examine how much these topics were covered in 15 of the most popular introductory psychology textbooks. Although some topics such as social relationships and well-being were discussed in nearly all textbooks, others were less frequently covered, including the validity of self-reported well-being, the effects of spending on happiness, and the impact of culture and society on well-being. We aim to ensure more complete coverage of this important area in psychology courses.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".