Who says there’s a problem? Preferences on the sending and receiving of prohibitive voice
Bibliographic record
Abstract
Which employees are likely to warn leaders about threats to the workplace? When employees do speak up, will these messages gain the leader’s interest? In this article, we rely on theories of power to predict how employee characteristics (work prevention regulatory focus, closeness to the leader (leader-member exchange) and rank) influence whether employees send messages about threats (prohibitive voice). We also explore whether employee characteristics (closeness to the leader and rank) affect leaders’ attention to threat messages. In a two-wave field study with 55 leaders and 214 employees, we found that leaders were more likely to show interest in messages about threats from employees who they were not close to, but who had high rank. However, only employees with a strong work prevention regulatory focus and/or those of higher rank were likely to prioritize the sending of such messages. Although we also expected that employees who had a good relationship with the leader would send more information about threats, we found they were less likely to do so. This research suggests that there may be “opaque zones” in organizations, places where employees are unlikely to warn leaders about threats and where leaders will not pay attention even if they do.
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.
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 | 0.003 | 0.020 |
| 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) | 0.004 | 0.001 |
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".