Monitoring and Evaluation of the Implementation Process of the State Policy to Promote the Development of Civil Society in Ukraine
Bibliographic record
Abstract
Among the components of the cycle of state policy to promote the development of civil society in Ukraine, the process of its implementation is studied. In particular, the current state of monitoring and evaluation (ME) of this process was analysed, scientific developments on this topic were reviewed and the authors' own approach to the implementation of state policy in this area was proposed. The ME of the process of implementation of the National Strategy for Civil Society Development for 2016-2020 in Ukraine (hereinafter – the National Strategy) as the main legal act that ensures the implementation of state policy in this area at national, regional and local levels of public administration, was studied. The basic requirements to the organisation of the ME of the process of implementation of the National Strategy in terms of periodicity, multilevel, organisational and methodological support, forms of control were formulated. A set of evaluation criteria and indicators was proposed and recommendations for the organisation of this process were provided. The provided recommendations can be the basis for the creation and implementation of the ME system for the implementation of the National Strategy. The results of the ME process of implementation of the current National Strategy can be used in the development of a new National Strategy for the next period after 2020.
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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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".