Repairing Trust to Preserve Balance: A Balance‐Theoretic Approach to Trust Breach and Repair in Groups
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We draw on balance theory ( H eider, 1958) to better understand trust breach in its social context. By focusing on the motive to preserve balanced relationships within groups, we present a novel view on how and when trust repair is likely to occur in teams and workgroups. We also argue why such balanced states are likely to be more than just transitory, and why people attempt to rebalance systems rendered imbalanced by a breach. In addition, by examining the balance motive in trust relations, we conjecture about when trust judgments and behavior are likely to converge (or diverge) among the members of teams and work groups. Our approach contributes to an emerging stream of literature on the role of third parties and social groups on conflict and trust in teams and workgroups. Throughout our analysis, we offer propositions to guide subsequent empirical research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it