EVALUASI PELAKSANAAN PROGRAM PENGEMBANGAN KAWASAN MINAPOLITAN DENGAN MODEL CIPP (CONTEX, INPUT, PROCESS, PRODUCT) DI KOTA BITUNG
Bibliographic record
Abstract
This study aims to evaluate the Implementation of Minapolitan Area Development Program with CIPP Model (Context, Input, Process, Product in Bitung City) This research was conducted from May until July 2016 in Bitung City The data collection method used is primary and secondary data. The analytical method used in this research is descriptive qualitative analysis which is used to clearly describe the condition of development of Minapolitan area in Bitung City Evaluation model used is CIPP Evaluation Model (Context, Input, Process, Product, Result) The result of the research is Context Evaluation (1) Based on the evaluation of the implementation context of the development program of Minapolitan area in Bitung City is very suitable if the City of Bitung stipulated by the decree of the minister of marine and fishery Number KEP.32 / MEN / 2010 because Bitung City meets the requirements of a region determined as gai area minapolitan one of which is a strategic location and natural resources available. (2) Based on input evaluais seen from several aspects, among others: (a) infrastructure aspect, (b) institutional aspect and regulation, (c) funding aspect, (3) Based on process evaluation, where the blue ocean port becomes the motto of the mover and the harbor continues to be developed to match the fishing port in the Philippines. (4) Based on product evaluation, the implementation of minapolitan program in Bitung City increases the volume and value of capture fisheries production in Bitung City.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".