The Impact of Relational Demographics on Perceived Managerial Trustworthiness: Similarity or Norms?
Bibliographic record
Abstract
Perceived trustworthiness is a critical antecedent of interpersonal trust, yet researchers have a limited understanding of how such perceptions are generated. The authors used 2 competing perspectives within the relational demography literature--similarity-attraction and relational norms--to empirically examine the effect of demographic differences. Whereas the similarity-attraction account suggests that subordinates will perceive their managers as more trustworthy when managers and staff are similar in demographic attributes, the relational norms account proposes that subordinates will perceive their managers as more trustworthy when their demographic differences follow normative expectations. Data collected from a field study of 178 manager-subordinate dyads in Hong Kong and Macau support the relational norms account in terms of education and organizational rank. The authors discuss the theoretical and practical implications of the study.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 teacher head, 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".