Criteria and Methodologies for Assessing Efficiency of Environmental Government Programs in the Russian Federation
Bibliographic record
Abstract
Existing approaches to performance evaluation for environmental government programs require improvement. In the Russian context, the obstacles to objective evaluation include: target indicators for state programs are not set according to SMART (Specific, Measurable, Achievable, Relevant, Time-bound) criteria; the importance of budget efficiency indicators for investment decision-making is underestimated; and, some approaches to ex post evaluation of government programs are oversimplified. Specific recommendations are given that would allow improvement of the methodology for ex ante appraisal and ex-post evaluation of environmental programs. A flowchart is developed to guide decision-making on whether to terminate or continue the program on the basis of its overall evaluation rating, which is calculated using a modified Program Assessment Rating Tool (PART), and the degree of conformity between actual and planned volume of financing. The flowchart represents a formalized procedure for the adjustment of the program implementation period and schedules for the achievement of target values for individual indicators; review of target indicator values; funding amounts and schedules; and change of management. A case study of two Russian environmental programs, Pure Water and Water Industry Development, is used to test the approaches recommended by the author. Full text available at: https://doi.org/10.22215/rera.v11i2.1190
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".