THE SMALL MEDIUM ENTERPRICES (SME) POLICY REVIW OF CITY \nGOVERNMENT OF YOGYAKARTA INDONESIA AND YUNLIN TAIWAN
Bibliographic record
Abstract
This study is aimed to investigate the issue of SME Policy of City Government. However, \nfew policy initiatives target high-growth SMEs. There are various model related those \nissues such as Korea and Singapore have recently implemented policies for high-growth \nSMEs, Israel plans to introduce such policies, Canada, combined SME support through \nR&D programs and venture capital increased the number of high-growth companies \nconsiderably. Global Review of Innovation Policy Studies indicates that there is a lack of \nevaluation studies that could substantiate certain measure to support high-growth SMEs as \nbeing particularly effective or ineffective in Indonesia. Indonesia and Taiwan is a very \ninteresting laboratory for this research, since the problems with governance, such as related \nparty lending or crony capitalism, is one of the institutional problems to high-growth SMEs \npolicies. This collaborative research is to provide an extremely comprehensive benchmark \nfor investigating the most obstacle to lead SME, parties involved, and policy modeling as \nwell as city government responsibility
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".