People prefer to diversify across different types of prosocial behaviour
Bibliographic record
Abstract
People have multiple opportunities to act prosocial any given day but only limited resources to do so (e.g. time, effort and money they are willing to invest). We test whether people prefer to diversify their prosocial efforts across different types of help: casual help, direct help, indirect help and emotional support. In two daily diary studies (total N = 711), we examine whether previous prosocial behaviour affects subsequent prosocial behaviour for the same or other types of prosocial behaviour. We found that day-to-day prosocial behaviours reflected a diversified helping pattern. Participants were less likely to help the same way (i.e. the same type of prosocial behaviour) on subsequent days and more likely to help in different ways (i.e. a different type of prosocial behaviour). This tendency did not extend to casual help in Study 2, implying that the next day reduction in doing the same type of prosocial behaviour is limited to prosocial behaviours that are at least somewhat effortful or time consuming.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".