An Evaluation of the Integrated Entrepreneurship Development Program (PKT) in Supporting Jakarta's Development Agenda
Bibliographic record
Abstract
As a flagship program of DKI Jakarta, the Integrated Entrepreneurship Development Program (PKT) has been established since 2017.Even though the program has been established for about four years, the impact or the implementation of the program is very limited and has not been much analyzed in recent studies.This article aims to describe and analyze the Context, Input, Process, and Product of formative and summative program evaluation.The methodology used is the mixed method with quantitative and qualitative approaches.Data were collected by using in-depth interviews, Focus Group Discussions with stakeholders, observation, spreading questionnaires for respondents, and analysis on the website and other sites relevant to the program.352 responses were received and analyzed through a simple descriptive statistic.The result shows that the level of achievement of the development of PKT is still in the category of moderately good with an average of 3.95 out of 5. DKI Jakarta government has considered some actions to enhance the quality of PKT by redesigning the program.However, the modification is still facing difficulties in practice, especially in terms of Input, Process, and Product since the alteration on the program is still focused on policy context.The findings have important implications for effective management in delivering entrepreneurs for the government.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".