Bad bosses and self‐verification: The moderating role of core self‐evaluations with trust in workplace management
Bibliographic record
Abstract
Abstract Who responds most strongly to supervisor social undermining? Building on self‐verification theory (Swann, 1983, 1987), we theorize that employees with positive views of the self (i.e., higher core self‐evaluations [CSEs]) who also maintain higher trust in workplace management are more likely to experience heightened stress and turnover intentions when undermined. We argue that this subset of employees (high CSE, high trust) are more likely to feel misunderstood when undermined by their supervisor and that this lack of self‐verification partially explains their stronger responses to supervisor undermining. We find initial support for the first part of our model in a study of 259 healthcare workers in the United States and replicate and extend our findings in the second study of 330 employees in the United Kingdom. Our results suggest that the employees Human Resources often wishes to attract and retain—employees with high CSE and high trust in workplace management—react most strongly to supervisor social undermining.
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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 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".