Bibliographic record
Abstract
Faultlines have the potential to significantly disrupt team performance due to the creation of intergroup bias. In geographically dispersed teams, given the combination of dispersed locations and other diversity characteristics, faultlines are potentially a major issue that needs to be more fully understood. This study examines the impact of faultlines on geographically dispersed teams and how problems caused by faultlines can be resolved. An experimental study of 40, four-person student teams finds that perceived faultlines heighten conflict and impair decision process quality. The findings also suggest that self-disclosure via weblogs and task elaboration can repair damage caused by faultlines. However, self-disclosure does not have a direct effect on reducing faultlines; the relationship is moderated by social attraction. That is, as team members disclose personal information to out-group members and out-group members are attracted to such disclosure, perceived faultlines are diminished. This study also finds that even in teams with strong perceived faultlines, team members are still able to exchange and integrate perspectives if they have a better understanding of their out-group members via self-disclosure. The negative consequence of faultlines therefore is eased when task elaboration occurs during task execution. Implications of these coping mechanisms for teams with faultlines in organizations 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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".