Employer silencing in a context of voice regulations: Case studies of non‐compliance
Bibliographic record
Abstract
Abstract This article, drawing on the latest insights into organisational silence, considers how employers seek to withhold information and circumvent meaningful workplace voice when confronted with regulatory requirements. It offers novel theoretical insights by redefining employer silencing as characterised by the withholding of information and the restriction of workplace dialogue. In outlining three principal routes of non‐compliance—avoidance, suppression, and neglect—we empirically illustrate the path to silence in the regulatory context of the European Union Directive establishing a general framework for informing and consulting employees. Rather than considering how employers utilised the regulations, as existing research considers, we look at how employers circumvented the regulatory space in three case studies in the United Kingdom and Ireland and the significant role of employer silencing as a tool for explaining this dynamic.
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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 | 0.029 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.006 |
| 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".