Enhancing the professionalism of civil servants: the experience of the OECD countries
Bibliographic record
Abstract
Professionalization of official activities is an important link in the process of strengthening the public service and increasing customer focus in relations between the public and private sectors. Time dictates higher demands on the professional and personal qualities of managers and ordinary employees of government agencies. Communication skills, flexibility in decision making, and analytical skills come to the fore. The aim of this article is to study the experience of OECD countries in this area, the main trends in the training and retraining of officials, with the development of recommendations for improving approaches to the professionalization of the state apparatus in Kazakhstan. The study examines and summarizes cases from countries such as the UK, USA, Canada, Japan, France, Lithuania, and Australia. In addition to the educational component, the policy of civil servants professionalization within the OECD countries is based on the meritocracy principles, gender equality, and continuous improvement of skills. Overall, the core principles for improving public service in OECD countries are professional, strategic and innovative. The paper concludes with recommendations aimed at advancing the professionalization of Kazakhstan's state apparatus, drawing on current practices and lessons learned from international best practices. Key words: professionalization, training, retraining, government apparatus, Kazakhstan, OECD.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".