When disadvantage becomes advantageous: A conflict expectation model of demographic faultlines
Bibliographic record
Abstract
The faultline literature has not been consistent about whether group faultlines are positively or negatively associated with group conflict. To address this inconsistency, we present a conflict expectation model of demographic faultlines and propose that demographic faultlines give rise to conflict expectations, which set faultline groups to deal more effectively with process conflict. In one experimental vignette study and two field studies, our results supported our conflict expectation model of demographic faultlines. In Study 1 we establish that there are higher conflict expectations in faultline compared to no-faultline teams. In Study 2, we found that demographic faultlines moderate the relationship between process conflict and group performance, such that in groups with faultlines, the negative association between process conflict and group performance is mitigated. In Study 3, we replicate and extend this finding by showing that a different operationalization of faultlines (faultline strength) mitigates the negative effect of process conflict on both a proximal group outcome (group cohesion) and a distal group outcome (group performance). Implications for the positive effects of demographic faultlines and managing process conflict in groups are discussed.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".