Understanding the Consequences of Property Rights Mismatches: a Case Study of New Zealand's Marine Resources
Bibliographic record
Abstract
Within fisheries and natural resource management literature, there is considerable discussion about the key roles that property rights can play in building biologically and socially sustainable resource management regimes.A key point of agreement is that secure long-term property rights provide an incentive for resource users to manage the resource sustainably.However, property rights mismatches create ambiguity and conflict in resource use.Though the term mismatches is usually associated with problems in matching temporal and spatial resource characteristics with institutional characteristics, I expand it here to include problems that can arise when property rights are incompletely defined or incompletely distributed.Property rights mismatches are particularly likely to occur over marine resources, for which multiple types of resource and resource user can be engaged and managed under a variety of regulatory regimes.I used New Zealand's marine resources to examine the causes and consequences of these property rights mismatches.New Zealand is particularly interesting because its property-rights-based commercial fishing regime, in the form of individual transferable quotas, has attracted considerable positive attention.However, my review of the marine natural resource management regime from a broader property rights perspective highlights a series of problems caused by property rights mismatches, including competition for resources among commercial, customary, and recreational fishers; spatial conflict among many marine resource users; and conflicting incentives and objectives for the management of resources over time.The use of a property rights perspective also highlights some potential solutions such as the layering of institutional arrangements and the improvement of how property rights are defined to encourage long-term sustainability.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".