The Measurement of Public Policy Assessment of North Sumatra Province, Indonesia
Bibliographic record
Abstract
This study attempts to explore the measurement model of public policy assessment of North Sumatra province, Indonesia.The research method employed in this study is a mixed-method of qualitative and quantitative approaches.The sample of this study is totaling to 100 participants.In data collection, questionnaires containing closed and semi-closed questions were used.Meanwhile, for qualitative data, interview was done to support quantitative data.From the analysis, it can be concluded that the purpose of evaluating the results of regional development plans is to ensure that regional development achievements are in line with established performance indicators.The regional apparatus planning agency of North Sumatra Province has only used budget realization as a metric for evaluating development planning thus far.Due to a lack of supporting data and qualified human resources in each regional apparatus organization, researchers discovered that regional apparatus organizations need help determining program and activity performance indicators.Several indicators, such as effectivity, adequacy, equity, responsivity, and accuracy must be developed in order to evaluate the success of a policy.In general, the inhabitants of North Sumatra believe that the development planning performance targets in North Sumatra are still low and have had little impact on the community welfare.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".