Literature Review : How to Examine the Mediating Factor of Government Role in Renewable Energy Adoption
Bibliographic record
Abstract
Indonesia is a blessed country with abundant renewable resources and the potential to manufacture up to 419 GWh of clean electricity for its people. The government plays a crucial role in shaping and nurturing the proper environment to support RE adoption and transition toward a sustainable economy. Various tools and dimensions are available to observe the government's role in accelerating RE adoption. However, studies show that the metrics used to examine Indonesia’s government regarding RE adoption are lacking. This provides an opportunity to improve the metrics by referring to countries worldwide that show success in the particular research field. The current study aims to address the challenge by presenting a literature review of studies on the government's role in RE adoption and extracting insight to develop improved metrics for examining Indonesia’s government's role in RE adoption. Furthermore, we use data mining to visualize the research map of the current topic to provide an understanding of which areas are underexplored. Thus, future studies can refer to the results to contribute further to the literature.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".