Machiavellian Traits in Public Service: Subordinate Silence and Workplace Abuse as By-products and the Moderation of Work Ethics among Public Servants in Anambra State
Bibliographic record
Abstract
Counterproductive work behaviours such as employee silence and workplace abuse are growing in Nigeria public service. Considering their possible negative consequences on organizational efficiency/effectiveness; this study examined subordinate silence and workplace abuse as by-products of Machiavellian traits and the moderation of work ethics. The study sampled 594 public servants in Anambra State, Nigeria whose ages ranged from 23yrs to 56yrs with a mean age of 39.50yrs using multi-stage sampling technique (purposive, cluster and simple randomization). After data analysis, the result revealed that there is high rate of Machiavellian traits, subordinate silence and workplace abuse (M=58.6; M=31.4; and M = 20.5 respectively) while there is low work ethics (M=21.2) among employees. Also, positive correlations were found among Machiavellian traits, subordinate silence and workplace abuse at r(594) = .58; .54; and .67, p < .05 respectively while negative correlation was found between work ethics and Machiavellian traits at r(594) = -.60 p < .05; between work ethics and subordinate silence at r(569) = -.49, p < .05 and between work ethics and workplace abuse at r(594) = -.72, p < .05. In the regression model, Machiavellian traits positively predicted subordinate silence at β = 1.21**, P < .01 and workplace abuse at β = .92, p < .05 (n = 594). Also, work ethics negatively predicted Machiavellian traits at β = -.879 p < .05 (n = 594). Furthermore, in model 2 and 3, work ethics was found to moderate only the relationship between Machiavellian traits and subordinate silence at β = .129*, p < .01 and between Machiavellian traits and workplace abuse at β = .191**, p < .01 (n = 594). Findings imply that unhealthy exchange and social climate has negative employee outcomes which affects organizational effectiveness of Nigerian public sector.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".