The impact of intergroup similarity on prosocial behaviour
Bibliographic record
Abstract
Individuals are frequently asked to provide aid to those in need and social networking sites have become a popular vehicle for requesting such aid. The question of who is likely to receive help has applied implications, and research addressing this in an online context is timely. This study therefore evaluated the impact of intergroup similarity on online prosocial behaviour. Intergroup similarity was manipulated by altering the national identity of a recipient of aid ingroup (Canada), similar outgroup (United States), and dissimilar outgroup (South Africa). Prosocial behaviour was assessed on three measures: Facebook support (clicking ‘like’ or ‘share’ on Facebook), prosocial intentions (willingness to engage in prosocial behaviours with real world consequences: signing a petition, volunteering, donating, or fundraising), and prosocial action (behaviours with real world consequences: signing a petition, volunteering, donating or fundraising). Moderated multiple regression analyses assessed whether prosocial personality, civic engagement, and conservatism moderated the relationship between intergroup similarity and the three measures of prosociality. Main effects and moderation effects were generally consistent with the common ingroup identity model. Implications are discussed in relation to increasing the effectiveness of charitable campaigns and educational programs aimed at promoting prosocial behaviour.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".