Political Dynamics of Innovative Policy Development in Managing Forest Fires in Riau Province
Bibliographic record
Abstract
This research examines the influence of political dynamics on the development of forest fire management policies in Riau Province, Indonesia, which is a serious environmental problem with broad impacts on ecosystems, health, and the economy.In the context of this research, innovative policy refers to public policies that contain new approaches or creative solutions that differ from conventional practices in their formulation, implementation, and evaluation.These policies aim to increase the effectiveness of forest fire management and strengthen community participation and collaboration between stakeholders.Using a qualitative approach, data was collected through in-depth interviews, observation, and analysis of policy documentation and historical data.This study involved 15 key informants selected purposively based on their strategic involvement.The informants comprised four local government officials, three legislative members, four environmental NGO leaders, and four local community representatives.The main findings in this research show that interactions between several political actors, such as the government, legislature, NGOs, and the private sector, strongly influence forest fire management policies in Riau Province.Although local governments have implemented policies such as Regional Regulation Number 1 of 2019 and the Fire Control Task Force, budget fluctuations and changes in political priorities ahead of elections often affect the effectiveness of these policies.The involvement of the legislature and the private sector has positively impacted technical and financial support and a negative impact on short-term political interests.Policy sustainability relies heavily on balancing political interests with environmental protection.To achieve this, innovative policy development can be carried out through strengthening multi-actor collaboration, utilizing advanced technology such as satellite monitoring, and accurate data-based policies, which enable more targeted interventions and more efficient responses to political dynamics and long-term sustainability.In conclusion, the success of forest fire management in Riau depends on the ability to maintain policy consistency despite changes in political dynamics, as well as the importance of strengthening multi-actor collaboration and utilizing technology in formulating sustainable and effective policies.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".