A Sociological Analysis of the Destructive Motivation of Public Servants: Causes and Avoidance
Bibliographic record
Abstract
The article deals with a study of the destructive motivation of public servants on the example of the Republic of Sakha (Yakutia). The study was conducted from 2015 to 2018. The purpose of study was to determine what factors are demotivating for civil servants. The research method was the questionnaire method based on a stratified quota sample. It has been proven that this type of motivation in governmental bodies is primarily caused by the employees’ disappointment in expectations. Public service is attractive mainly due to the stability of employment and the high social status of an official. However, public servants have to deal with a lack of real power, a high level of stress, and work intensity. Also, the respondents consider their income as inadequate to their work and social status. As a result, employees tend to minimize their labor costs, and destructive motivation appears. The practice of personnel management applies several measures to eliminate this phenomenon, but not all of them can be implemented for the public service. The authors regard the following means as the most effective ones, namely: the improvement of labor organization, the automatization of routine operations, personal responsibility increase, and the development of decision-making discretion.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".