Student pro-sociality: Measuring institutional and individual factors that predict pro-social behaviour at university
Bibliographic record
Abstract
Students operate within a bounded social context and often face decisions regarding whether to pursue selfish or group-level benefit. Yet little work has examined what predicts their behaviour towards fellow students. This work addresses this gap by investigating what factors may predict students’ performance of pro-social actions at university, and how an institution may maximise such behaviour. Study 1 created the student pro-sociality scale, used to measure these tendencies in students. In study 2, 428 students from 25 UK universities took part an online survey study using this scale, and several other pre-existing measures of possible predictors. Analysis suggested that of those factors examined, role clarity, affective commitment, empathy, and perspective-taking emerged as the most influential. This first foray into this area can now inspire further research in finding the effective ways of fostering pro-social behaviour in students.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".