Dinamika Kebijakan REDD+ Di Kalimantan Tengah: Dari Implementasi Awal (2007–2012) Hingga Reaktivasi Tahun 2025
Bibliographic record
Abstract
Program Reducing Emissions from Deforestation and Forest Degradation (REDD+) merupakan salah satu upaya global untuk menurunkan emisi karbon dari sektor kehutanan. Kalimantan Tengah menjadi provinsi percontohan pelaksanaan REDD+ di Indonesia sejak tahun 2007 melalui berbagai inisiatif pemerintah dan lembaga internasional. Penelitian ini bertujuan untuk menganalisis dinamika implementasi awal REDD+ di Kalimantan Tengah pada tahun 2007, mencakup kebijakan, aktor pelaksana, serta tantangan yang dihadapi. Metode penelitian menggunakan pendekatan deskriptif kualitatif dengan studi literatur dan analisis dokumen kebijakan. Hasil penelitian menunjukkan bahwa tahun 2007-2012 merupakan fase kesiapan REDD+, tahun 2013-2014 merupakan fase implementasi REDD+, tahun 2016-sekarang merupakan fase pembasaran berbasis hasil. Namun, keterbatasan koordinasi antar lembaga, kapasitas teknis, serta sinkronisasi kebijakan pusat-daerah menjadi kendala utama. Implementasi awal ini memberikan dasar bagi pengembangan kebijakan REDD+ di tahun-tahun berikutnya. KATA KUNCI : Emisi Karbon, Implementasi Program, Kalimantan Tengah, Kebijakan Kehutanan, REDD+
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".