Reconsidering prosocial behavior as intersocial: A literature review and a new perspective
Bibliographic record
Abstract
Abstract Research questions in the prosocial behavior literature focus on the pro aspect of prosocial behavior—that is, how to motivate actions that benefit others. These questions typically employ simplified decision contexts that neglect the intersocial aspect of prosocial behavior—that is, people are embedded in social networks and impacted by interactivity among two or more persons, entities, or societies. These intersocial influences have increased with technology access. Consumers now face richer choice tradeoffs, can access more information on causes, observe others' actions, and choose to make their own choices public. To ask questions that address the nature of prosocial behavior itself rather than consider it merely as another decision context to motivate human behavior in, we call for researchers to conceptualize prosocial behavior as intersocial . This approach can help capture the more realistic decision tradeoffs consumers face, as well as illuminate new research opportunities arising from considering technology‐enabled giving and socially hyperconnected consumers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".