Honor among thieves: The interaction of team and member deviance on trust in the team.
Bibliographic record
Abstract
In this article, we examine member trust in deviant teams. We contend that a member's trust in his or her deviant team depends on the member's own deviant actions; although all members will judge the actions of their deviant teams as rational evidence that they should not be trusted, deviant members, but not honest members, can hold on to trust in their teams because of a sense of connection to the team. We tested our predictions in a field study of 562 members across 111 teams and 24 organizations as well as in an experiment of 178 participants in deviant and non-deviant teams. Both studies show that honest members experience a greater decline in trust as team deviance goes up. Moreover, our experiment finds that deviant members have as much trust in their deviant teams as honest members do in honest teams, but only in teams with coordinated rather than independent acts of deviance, in which deviant members engage in a variety of ongoing dynamics foundational to a sense of connection and affective-based trust. (PsycINFO Database Record
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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 | 0.004 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 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".