Employee silence in the context of unethical behavior at work: A commentary
Bibliographic record
Abstract
This special issue of the German Journal of Human Resource Management reflects and reinforces the growing global interest in organizational studies of employee silence. Attention to this multidimensional concept has been steady since its introduction as the withholding of expressed evaluations of work circumstances to persons able to effect change with two initial dimensions - quiescent silence (fear and anger-based) and acquiescent silence (futility and resignation-based) - following unjust events (Pinder & Harlos, 2001). However, an unprecedented confluence of trends across law, justice, and governance worldwide underlies current strong concern about employee silence of ethical issues in organizations, including how we understand (un)ethical work behavior and protection for employees, organizations, and society. To sustain interest and impact, rigorous and relevant research is needed. This calls for intellectual diversity and open-mindedness to spur studies of employee silence while resisting paradigmatic isolation or privilege, concept proliferation and confusion, level-of-analysis slippage (e.g., equating employee silence with organizational silence), and other challenges. Advancing employee silence-ethics linkages depends on expanding theories using multiperspective, integrative approaches and testing models that span social, cognitive, and emotional elements in processes of silence. Better knowledge of employee silence promises a more healthy and motivated workforce, more successful and sustainable organizations, and more vibrant and engaged societies.
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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 | 0.015 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.067 | 0.069 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".