The Effect of Personal Disclosure within Teams: Can Faultlines in Geographically-Dispersed Teams Be Bridged?
Bibliographic record
Abstract
Differences between members within teams can align to create potential faultlines. Faultlines have the potential to significantly disrupt performance due to the creation of intergroup bias. In geographically-dispersed teams, due to dispersed locations and other diversity characteristics, potential intergroup bias could be a major issue in Global Virtual Teams (GVT's) that needs to be more fully understood. This study examines whether public self-disclosure via weblogs can alleviate intergroup bias in geographically-dispersed teams by enabling the development of personal relationships. An experimental study of 34 4-person student teams found that public self-disclosure does not have a direct effect on reducing intergroup bias but has a mediated effect through social attraction. That is, as team members disclose their personal information and out-group team members are attracted to such disclosure, perceived in-group and out-group differences are diminished. Overall, findings support the common in-group identity model and highlight the potential benefits of socialization via weblogs in dispersed teams.
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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 | 0.005 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".