Encouraging civil servants to be frank and fearless: Merit recruitment and employee voice
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
Recruiting civil servants on the basis of merit is believed to improve the quality of governance because it increases the bureaucracy's expertise, leads bureaucrats to develop distinct preferences and encourages them to candidly voice their opinions to others. Yet, to date, the reason why merit recruitment positively affects employee voice remains theoretically vague and has received little empirical scrutiny. This article advances this research by theoretically specifying why merit recruitment positively affects employee voice, and by empirically testing this association with survey data measuring the perceptions of federal civil servants in Canada. Controlling for several additional factors believed to influence employee voice, the results from various multivariate regression models show a robust and statistically significant association between merit recruitment and fear to voice a dissenting opinion. The more civil servants believe that merit recruitment is high, the less they fear reprisal for expressing a dissenting opinion to their superiors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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