Similarly Torn, Differentially Shorn? The Experience and Management of Conflict between Multiple Roles, Relationships, and Social Categories
Bibliographic record
Abstract
In three studies we examined the experience and management of conflict between different types of multiple identities. Participants described a conflict between pairs of role, relational, or social identities before rating the experience (i.e., magnitude, stress, growth) and management of conflict on a newly developed scale assessing four strategies: reconciliation, where identities are integrated, realignment, where one identity is chosen over another, retreat, where both identities are avoided, and reflection, where fit (with others, situation) determines identity selection. In general, the types of identities mattered for conflict management but not its experience: Magnitude and growth did not differ, however stress was greater for role identity conflicts (Study 3 only) and participants endorsed the use of more realignment for role conflicts (Study 2) and more retreat for relational conflicts (Study 3) relative to other types of identity conflicts. Furthermore, findings suggested that the perceived flexibility of identities, not their importance or valence, were associated with realignment and retreat for roles and with retreat for relationships. Experiencing conflicts between multiple identities leaves people similarly torn, but multiple roles and relationships may be differentially shorn to manage conflict.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".