An evolutionary psychology perspective on gift giving among young adults
Bibliographic record
Abstract
Abstract With evolutionary psychology used as the theoretical framework, two aspects of gift giving among young adults are investigated: (a) sex differences in motives for giving gifts to a romantic partner, and (b) the allocation of gift expenditures among various relations, including romantic partners, close friends, close kin, and distant kin members. As per the evolved sex differences in mating strategies, it is proposed and found that men report tactical motives for giving gifts to their romantic partners more frequently than women. Also, there are no sex differences in situational motives for giving gifts. In addition, women are aware that men use tactical motives more often; whereas men think that these motives are employed equally by both sexes. With regard to gift expenditures it is found that, for kin members, the amount spent on gifts increases with the genetic relatedness (rvalue) of the particular kin. When all relations (kin and nonkin members) are included, the allocation of gift expenditures were the highest to romantic partners, followed by those to close kin members and then to close friends. The latter finding is explained via the importance attached to the evolved psychological mechanisms linked to each of the above relations, namely, reproductive fitness (for partners), nonreproductive fitness (for close kin members), and reciprocal altruism (for close friends). © 2003 Wiley Periodicals, Inc.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".