Research on performance evaluation of public administration departments based on Rao-Stirling diversity algorithm
Bibliographic record
Abstract
This investigation delves into public administration sector performance, leveraging the Rao-Stirling diversity algorithm, traditionally utilized in assessing the interdisciplinarity of scientific research.The study repurposes this algorithm for public management, enabling the quantification and nuanced analysis of diversity in policy execution and service provision.Initially, we establish a suite of pivotal public management performance metrics encompassing service quality, efficacy of policy implementation, and equity in resource distribution, segmenting these into various evaluative dimensions.Subsequently, the Rao-Stirling algorithm is employed to dissect the variances and synergies across these dimensions and their cumulative effect on overall performance.The results illuminate the complex web of interrelations among distinct administrative functions and services, offering profound implications for enhancing public management's efficiency and effectiveness.Moreover, the study critically assesses the Rao-Stirling algorithm's applicability and constraints in gauging public management performance, thus contributing novel insights and methodological recommendations for future inquiries within this domain.
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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 | 0.029 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".