Improving the System for Evaluating the Efficiency and Performance of Civil Servants
Bibliographic record
Abstract
This article addresses the pressing issue of improving the system for evaluating the efficiency and performance of civil servants within the context of public administration development and enhancing the quality of public services. The aim of the study is to develop proposals for modernizing the existing evaluation system for civil servants based on a fundamental analysis of theoretical approaches, domestic practices, and international experience. The work presents a comprehensive analysis of both the key categories and approaches used in this field, as well as the current regulatory and legal framework. The current state of Russia’s civil servant evaluation system is examined, revealing key issues such as procedural formalism, subjectivity, weak connection with HR processes, insufficient evaluator competence, and underdeveloped information and analytical systems. Advanced international practices in civil servant evaluation are reviewed, including those in the United States, the United Kingdom, Germany, France, and Canada. The article proposes main directions for improvement: the implementation of results-based management using key performance indicators (KPIs); the development of a competency-based approach; digitalization of evaluation procedures; and strengthening the link between evaluation outcomes and motivation mechanisms. The study’s methodology is grounded in scientific methods such as analysis and synthesis. The results can be used in the modernization of the civil servant evaluation system, as well as in the development of legal and methodological documents in this field.
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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 | 0.123 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".