Family Matters: Rethinking the Psychology of Human Social Motivation
Bibliographic record
Abstract
What motives do people prioritize in their social lives? Historically, social psychologists, especially those adopting an evolutionary perspective, have devoted a great deal of research attention to sexual attraction and romantic-partner choice (mate seeking). Research on long-term familial bonds (mate retention and kin care) has been less thoroughly connected to relevant comparative and evolutionary work on other species, and in the case of kin care, these bonds have been less well researched. Examining varied sources of data from 27 societies around the world, we found that people generally view familial motives as primary in importance and mate-seeking motives as relatively low in importance. Compared with other groups, college students, single people, and men place relatively higher emphasis on mate seeking, but even those samples rated kin-care motives as more important. Furthermore, motives linked to long-term familial bonds are positively associated with psychological well-being, but mate-seeking motives are associated with anxiety and depression. We address theoretical and empirical reasons why there has been extensive research on mate seeking and why people prioritize goals related to long-term familial bonds over mating goals. Reallocating relatively greater research effort toward long-term familial relationships would likely yield many interesting new findings relevant to everyday people's highest social priorities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".