Exit Strategy: How Will Forest Investment Program II (FIP II) Go Forward in Central Sulawesi?
Bibliographic record
Abstract
The Climate Investment Funds are putting into action one of the strategic funding programs that deal with climate change.This program is called the Forest Investment Program (FIP) II in Indonesia, which exists in ten Forest Management Units (FMUs), including Dolago Tanggunung in Central Sulawesi.The program will entice investment in the FMU and terminate in 2022.This research will investigate the prospects for the program's sustainability in the FMU of Dolago Tanggunung using a risk management approach and a literature review.The results of this study show that the FMU Manager will keep doing what funding has already done.Even though the budget isn't massive, there are three priority programs: 1) KRC operationalization, 2) digital-based innovations, and 3) revision of the long-term forest management plan.But several programs will be added to Social Forestry to support the independent community empowerment that has already been done.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".