Structural Foundations of Concrete Outcomes: Embeddedness Fosters Gossip, Trust, and Performance
Bibliographic record
Abstract
Prior work has shown that employees are more likely to trust colleagues that are more embedded in their professional network. The prevailing assumption is that embeddedness serves a social monitoring function: the more third-party connections employees share, the less they would risk engaging in behaviors that might destroy trust, such as lying and cheating. We propose that embeddedness not only deters behaviors that are harmful to trust, but also serves as a structural foundation for behaviors that establish and increase trust, such as gossip. We thus propose a structural theory of gossip. Data from field studies across three organizations in Mainland China supported the hypothesis that gossip mediated the positive relationship between embeddedness and trust. The higher levels of trust linked to embeddedness and gossip was associated with positive downstream consequences in colleagues’ peer performance evaluations of the gossiper. We further discuss the role of gender in moderating trust relationships developing from positive and negative gossip. This research underscores the role that seemingly passive network structures can play in shaping concrete behaviors that have important interpersonal and organizational outcomes.
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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 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".