Indonesian Policy Campaign for Electric Vehicles to Tackle Climate Change: Maximizing Social Media
Bibliographic record
Abstract
Recent policies have encouraged the Indonesian government to campaign for the use of electric vehicles to prevent climate change problems. That prompted this study to analyse the model of government policy campaigns on social media. This study used a quantitative approach with descriptive content analysis. Data sources come from Twitter search results focusing on official government accounts (@jokowi) and keywords (electric vehicles and climate change). The analysis tool used is Nvivo 12 Plus. This study found that the government's use of social media can educate and influence public response to support government policies on the use of electric vehicles, including climate change issues. Reducing carbon dioxide (CO2) emissions, the potential for developing an ecosystem for electric vehicles, encouraging investment, increasing state revenues, promoting public involvement and participation, and offering subsidies are some of the significant issues that the government has been actively promoting on social media. The government raises awareness of this issue to sway public opinion and encourage the adoption of regulations that will facilitate the usage of electric vehicles. It is also possible to contribute to the initiative to raise public awareness of the significance of environmental and sustainability-related concerns in the future.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".