Why won’t people speak up? Unpacking silence at work
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper examines employee silence, namely, the causes of silence and how it might be overcome. Design/methodology/approach Drawing from academic research and work with organizations, the author explains that workplace diversity is insufficient to guarantee the contributions of diverse voices. The author then provides an overview of why individuals choose to remain silent and explores aspects of organizational culture and climate that contribute to silencing behaviors. Finally, the author offers suggestions on how organizational leaders can overcome silence. Findings The findings suggest that employee voice can be activated through a psychologically safe working environment in which leaders adopt a learning mindset, practice humility, create opportunities for all team members to contribute, treat people with fairness and respect, and hold others accountable to do the same. The findings also indicate that leaders can support safe and inclusive working environments by challenging basic assumptions and accepting vulnerability. Originality/value This paper makes an important contribution to the field of organization development and change by providing suggestions for how organizations can address workplace concerns and enhance performance by removing the inhibitors of “employee voice”.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it