Evaluating the utility of common-pool resource theory for understanding forest governance and outcomes in Indonesia between 1965 and 2012
Bibliographic record
Abstract
While Common Pool Resource (CPR) theory has been widely applied to forestry, there are few examples of using the theory to study large-scale governance. In this paper we test the applicability of CPR theory to understanding forest governance and outcomes in Indonesia between 1965 and 2012. Indonesia contains one of the world’s largest tropical forests, and experienced rapid deforestation during this time frame, with forest cover dropping from close to 85% to less than 50%. Using a mixture of within case comparison and process tracing methods, we identify key variables that influenced the levels of deforestation during two time periods: before 1998, when governance was dominated by the dictatorship of President Suharto, and after 1998, when democratic governance and political decentralization were initiated, and deforestation rates fell and then rose again. Our results point to the value of CPR theory in identifying important variables that influence sustainability at large scales, however they also illustrate important limitations of CPR theory for the study of forests with large spatial extent and large numbers of users. The presence and absence of key variables from CPR theory did emerge as important causes of deforestation. However, some variables, such as strong leadership and local rule-making, appeared to work in the opposite direction as predicted by CPR theory. In addition, key variables that may have influenced deforestation rates are not well captured in CPR theory. These include the intention of the governance system, the presence of clientelistic politics, the influences of international politics and markets, and the influence of top-down governance. Given that CPR theory does not fully explain the case at hand, its applicability, as is, to large-scale commons should be treated with some caution.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".