Formative evaluation of the regional development planning policies: A study on the consistency of the regional development planning in Kebumen regency
Bibliographic record
Abstract
This study aims to describe, analyze, and interpret the role of multi-set data consistency in regional development planning in Kebumen Regency, Central Java Province, Indonesia. This study uses qualitative research methods. The selection of informants used purposive and snow-ball sampling. Data collection uses in-depth interviews, observations, and documentation. Data analysis was carried out through open coding, axial coding, and selective coding procedures. The validity of the data is determined through an examination technique based on the criteria of credibility, transferability, dependability, and confirmability. The results of the study show: (1) Regional development planning policies, which regulate the process and stages of the preparation of regional long-term development plans/RPJPD, regional medium-term development plans/RPJMD, and regional development work plans/RKPD were developed in the process of preparing the three regional development planning documents of Kebumen Regency to meet local needs; (2) The process of preparing the 2005–2025 RPJPD, 2006–2010 RPJM, 2010–2015 RPJMD, and 2006 to 2014 RKPD uses a technocratic approach that emphasizes the importance of multi-sector data (3) Factors that affect the process of preparing and documenting regional development planning documents are compliance in the hierarchy of multi-sector existing data administration, the development of regulations for the implementation of regional development planning policies, the existence of existing multi-sector planning data documents national, provincial, and regional development documents related to development planning, as well as the use of technocratic approaches; (4) The 2010–2015 RPJMD is inconsistent with the 2010–2015 RPJPD.
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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 | 0.193 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".