Property rights play a pivotal role in the distribution of ecosystem services among beneficiaries
Bibliographic record
Abstract
Property rights are fundamental institutions that set the rules for who is allowed to use, manage, and control natural resources. Though the literature on property rights over natural resources is well developed. However, our understanding of the ways by which property rights govern actors’ ability to obtain ecosystem services provided by these natural resources remains under-explored. Using the Adirondack Park, USA, as a case study, we develop a framework that pairs property rights theory with spatial analysis to show who can obtain ecosystem services across this landscape. We look at rights over three ecosystem services: timber, drinking water and recreational fishing. We show that property rights combined with ecosystem service flow affect who can receive ecosystem services, and where, across the landscape. Our results demonstrate that property rights can play a pivotal role in who can obtain ecosystem services across landscapes. However, more work is required to model the supply and flow of ecosystems services, and to connect these to property rights to fully capture the interactions occurring between property rights and ecosystem services, and how they influence who can obtain these services. This paper contributes to the literature by showing how property rights influence who the potential beneficiaries of ecosystem services are under different property rights regimes.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".