Bibliographic record
Abstract
Is it helpful to be religious?The three target articles focus on this question.The favored answer, "it depends," seems a foregone conclusion given the amorphous nature of both the "independent variable", religion (the many different brands offer contrasting behavioral and spiritual advice), and the "dependent variable", well-being (which is operationalized in a variety of ways, including ability to cope with stress, success in social relationships, physical and psychological health, and mortality), as well as the differing quality of the samples and measures.The authors decry the lack of research on religion.What strikes us is not the dearth of relevant research-the literature reviewed in the target articles is far from negligible-but the focus on the costs and benefits of religion.Such research can help delineate which types of religious practices affect which outcomes, and all three articles take important steps in this direction.There are additional issues that could be explored profitably, however.Religion embodies the wisdom of the ages and has attracted the attention of some of the most gifted thinkers throughout history.As a result, religious texts and discussions provide a rich store of hypotheses about human behavior that that could be subjected to scientific test by research psychologists.Our own current research interest is in people's views of themselves through time.We have developed a theory of temporal self-appraisal (Ross & Wilson, 2000; Wilson & Ross, in press) that examines how people evaluate past selves and how those evaluations affect their
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.400 | 0.135 |
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".