Multiple Identity Configurations: The Benefits of Focused Enhancement for Prosocial Behavior
Bibliographic record
Abstract
This paper introduces a configurational approach to the study of multiple identities. Specifically, it examines how prosocial identity combines with collective and individualistic identities in conflicting and enhancing ways to affect prosocial behavior in organizational settings. We examine an unexplored intuition in the multiple identities literature that when all identities are enhancing (a mutual enhancement configuration), it will be best for prosocial outcomes. Our results show, however—across two field studies and two experiments—that enhancement between prosocial and collective identities (a focused enhancement configuration) results in the highest levels of prosocial behavior. Furthermore, we trace this result to the greater self-serving orientation activated in a mutual enhancement configuration, where one’s individualistic identity enhances one’s other identities. Our work demonstrates the value of a configurational approach to the study of multiple identities, and it challenges the assumption that a mutual enhancement configuration is always desirable. The online appendix is available at https://doi.org/10.1287/orsc.2017.1129 .
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".