Deforestation in Indonesia: The Politics of Land Use Change Post Suharto
Bibliographic record
Abstract
Over the past 25 years over 130 million hectares of natural forest land on our planet has been lost accelerating climate change and threatening the world’s most diverse ecosystems. Although the annual rate of global deforestation is half of what it was in the early 1990s it remains problematic in several regions of the world. Indonesia, for example, currently accounts for nearly 25% of global deforestation annually and has shown no signs of improvement. This thesis explores some of the key drivers of deforestation in Indonesia by making use of a rich dataset that tracks forest loss across eight years when Indonesia was undergoing political restructuring following the collapse of the Suharto dictatorship. Previous literature has pointed to the expansion in the number of political jurisdictions as a vehicle for increased political corruption which in turn could cause deforestation. The hypothesis is that when a new district is created there is increased competition for the sale of logging permits within a provincial wood market. This may incentivize district governments to issue more than the legal quota of permits consistent with Cournot-style competition. However, the data does not seem to line up with this argument. Instead, forest loss in Indonesia appears to be related to widespread forest fires caused by landowners for the purposes of clearing land primarily for palm oil plantations. The results from this thesis lay the groundwork for future research to focus on the determinants of growing demand for palm oil such as international trade.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".