The Property Rights Index (PRIF) can be used worldwide to compare different forest governance systems
Bibliographic record
Abstract
The bundle of forest landowners' rights largely varies from one jurisdiction to another. On a global scale, the diversity of forest management regime and property rights systems is such that finding comprehensive and standardised approaches for governance analysis purposes is a challenging task. This paper explores the use of the Property Rights Index for Forestry (PRIF) as an analytical tool based on five rights domains (access, withdrawal, management, exclusion, and alienation) to assess how regulatory frameworks impact the owners' forest property rights. We show that PRIF is a reliable index for various governance arrangements, considering its ability to score forest owners' freedom to decide in case studies that range from the Amazon area (Brazil), Misiones province (Argentina) and Quebec (Canada) to community-managed Nepalese and Mexican forests. PRIF scores obtained in these diverse governance arrangements confirm that the governance of forests held by entities other than the state is driven by two factors: the owner's ability to exclude the public from the use of his/her own resource and the owner's freedom to decide on the forest management goals. These factors explained 66.44% of the variance in our sample and should be considered as the main potential drivers while implementing any new international or national policy. Despite having a few limitations, the PRIF is a promising governance indicator and has been proven to perform well for various socioeconomic and legal contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".