A 10 Nation Exploration of Trustworthiness in The Workplace
Bibliographic record
Abstract
In the context of the workplace, and especially in today’s often fast-paced, cross-cultural and virtual work environment, a basic type of trust—“swift trust”—forms quickly based on cognitive processes and beliefs, or stereotypes, of another. Interpersonal trust is in large part based on these contextualized assessments of the extent to which another person is trustworthy. While trust across cultural boundaries has been examined, there is a lack of research investigating how trustworthiness is determined cross-culturally, especially with respect to what heuristics are used in the development of trust. The current project explored how trustworthiness is conceptualized and described for both colleagues and supervisors across 10 nations using the Stereotype Content Model. Qualitative descriptors of trustworthy supervisors and colleagues were coded based on the importance ascribed to warmth and competence, and these codes were used as the basis for cluster analyses to examine similarities and differences in descriptors of role-based trustworthiness. Both differences and similarities in the expectations of trustworthiness were found across the national samples. Some cultures emphasized both warmth and competence as equally important components to developing trustworthiness, some emphasized only warmth, while others emphasized only competence. Variations of trustworthiness stereotypes were found in all but two national samples based on role expectations for supervisors and colleagues. Data from the GLOBE project related to societal cultural practices and cultural leadership prototypes were drawn on to discuss findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 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".