When the whole self gives less than the parts: Multiple identities and prosocial task performance
Bibliographic record
Abstract
Bringing one’s whole self into the organization is an appealing narrative for both individuals and organizations, particularly non-profit organizations which are trying to meld task performance and altruistic motives. In this paper, we examine how and when bringing one’s whole self to bear on a prosocial task can be beneficial. We rely on a mixed method study of participants in an international charity sport event to show that, depending on the mixture of enhancement and conflict among the same set of identities, outcomes, in terms of task performance and attitudes towards the organization, vary significantly. First, an analysis of repeated interviews with 14 participants illustrates how individuals experience enhancement and conflict among identities. Second, through an analysis of longitudinal pre- and post-event survey with 69 participants, we identified different clusters of participants and find that those who experienced high enhancement within a subset of their identities, but the least amount of conflict overall, exhibited greatest task performance. In contrast, participants who experienced the highest enhancement across all their identities despite some levels of conflict, held the most positive attitudes towards the organization. We discuss the theoretical implications of examining the intra-individual relational context of identities for organizational scholarship.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".