The progress on governing REDD+ in Indonesia
Bibliographic record
Abstract
Indonesia is one of the ten most forest-rich countries in the world. Almost 70 per cent of Indonesia’s mainland is covered with forest. However, Indonesia faces one of the highest rates of forest loss in the world. Deforestation and forest degradation accounts for more than 60 per cent of carbon emissions in Indonesia. Being aware of that fact and the danger of climate change, in October 2009, Indonesia voluntarily committed to reduce emissions by 26 per cent from business as usual by 2020 through national efforts, and by 41 per cent with international support. Indonesia’s commitment has gained international support; chiefly from Norway, which signed a Letter of Intent on 26 May 2010. To formalise the commitment, Presidential Decree No 19/2010 on Task Force for the preparation of REDD+ Agency and Presidential Instruction No 10/2011 on moratorium on new licenses and improvement of natural primary forest and peat land governance have been issued. The Presidential Decree ended on 30 June 2011 and was continued by Presidential Decree No 25/2011, which was later amended by Presidential Decree No 05/2013. The third Presidential Decree will conclude in the middle of 2013. The expected outputs are: establishment of a New REDD+ agency; measurement, reporting and verification instrument; funding instrument; improvement on forest governance, including legislative reform, law enforcement and administrative procedures; and gazetting forest areas and consolidating licenses through legal audit and legal compliance or legal due diligence in the pilot province. The new REDD+ Agency is expected to be established in 2013. The Agency will be an independent central agency, directly responsible to the President of the Republic of Indonesia, and will be responsible for leading and coordinating the national effort to reduce the country’s carbon emission.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".