REGULATION AND DEREGULATION: PROPERTY RIGHTS ALLOCATION ISSUES IN THE DE REGULATION OF COMMON POOL RESOURCES
Bibliographic record
Abstract
Rights-based institutions have been adopted for certain natural resources in order to more effectively mitigate the losses of the common pool. Past central government (command and control) regulation has not proved satisfactory. In deregulation, a major issue has been the assignment of those rights and controversy over it has slowed the process. In this paper, I examine three different allocation rules: first-possession, lottery or uniform allocation, and auction and draw predic tions as to when they might be adopted and why they are controversial. I analyze the assignment and nature of the rights granted for common-pool resources where deregulation has occurred: oil and gas unit shares, emission permits, and selected fishery ITQ’s in six countries (Australia, Canada, Chile, Iceland, New Zealand, and the U.S). I find that firstpossession rules dominate where there are incumbent users. Lotteries and auctions are rarely used. I discuss criticisms of first-possession rules and argue that first-possession is likely more efficient than previously recognized. Accordingly, restrictions on such allocations as part of deregulation (rights set-asides for particular groups and exchange limitations) may be costly in the long run for addressing the problems of the common pool.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".