Buffering Against the Detrimental Effects of Demographic Faultlines: The Curious Case of Intragroup Conflict in Small Work Groups
Bibliographic record
Abstract
Abstract Group faultline literature suggests that subgroups impede group functioning. We propose that team conflict may buffer the detrimental effects of faultlines on group performance. We draw on social categorization and group process theories suggesting that the negative effects of fault‐lines are due to increased competition and decreased communication across subgroups and can be diminished with cross‐subgroup information exchange and elaboration. We propose that intragroup conflict in small groups will decrease negative effects of demographic faultlines because detecting conflict and engaging in conflict management require cross‐subgroup communication and information elaboration. In Study 1, using student groups we found that relationship, task, and process conflict buffered the negative effect of demographic faultline strength on group performance. In Study 2, we manipulated conflict and group faultlines (ethnic faultlines vs. no faultlines) and found that group conflict buffered the negative effect of faultlines on group performance. Theoretical contributions and practical implications 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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".