A Competency Model of Inclusive Governance: A Human-Centered Approach to Civil Service in Kazakhstan
Bibliographic record
Abstract
In the context of the transformation of public administration systems, inclusive and human-centered approaches that focus on addressing the needs of citizens and increasing the participation of various social groups in decision-making are becoming increasingly important. The purpose of this article is to develop a conceptual model of civil service competencies that support the implementation of inclusive and human-centered governance within the framework of the “Listening State” concept. The methodology of the study includes an analysis of strategic and regulatory documents of the Republic of Kazakhstan, as well as a comparative review of international practices from the United Kingdom, Canada, and New Zealand. The practical implementation of the model involves the development of behavioral indicators that take into account regional and cultural specificities, the introduction of mandatory training programs on inclusive leadership, intercultural communication, and digital literacy, as well as the establishment of regular competency assessments using adapted 360-degree feedback methods. Additional important steps include strengthening interagency coordination and developing digital citizen feedback tools to enhance openness and engagement. At the same time, potential risks such as resistance to change, limited funding, and institutional inertia may arise, requiring strong leadership support and a phased implementation plan with continuous monitoring. The results can be used to revise approaches to the selection, training, and evaluation of civil servants and to guide the development of a new civil service model that is focused on citizens’ needs and the promotion of human-centered governance.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".