Address for manuscript correspondence
Bibliographic record
Abstract
Acknowledgements: The authors would like to express their sincere thanks and appreciation to Dr. Michael Hoy, Dept. of Economics, University of Guelph, for many helpful discussions of and insightful comments on the analytical model. This work was made possible through a grant from the Richard Ivy Foundation, Toronto Ontario, and a dissertation fellowship from Resources for the Future. 1 Conservation Contracts under Asymmetric Information This paper analyses incentive problems involved in private management of publicly owned natural resources. The paper contributes to defining the regulatory role in creating an optimal information environment to maximize welfare from joint public and private good provision. Contract structures are developed to induce optimal conservation of public goods and services under differing assumptions about adverse selection and moral hazard. The associated contracting costs are increasing in the degree of information asymmetry, the total cost of conservation, and the difference in conservation costs across sites with different conservation values. The results imply that conservation contracts to mitigate moral hazard and adverse selection are welfare improving if efficiency gains from private management outweigh contracting costs induced by information asymmetries between regulator and the private sector managers. During institutional transformations, the regulator can choose to retain specific types of information, or to invest in monitoring activities, to reduce the costs of asymmetric information. 2 Conservation Contracts under Asymmetric Information 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".