Bibliographic record
Abstract
There is almost universal agreement that the most effective solution to open-access natural resource problems lies in some form of ownership.Authors disagree on the secondary question of which ownership form, i.e., private, community, or government, will produce the most efficient or equitable results under particular conditions.There has been little attention paid to the fact that government ownership, that is, regulation, is certain to produce results that all interested subsets of the public will view as inefficient and inequitable.Dissatisfaction flows inevitably from the requirements and realities of democratic decisionmaking structures and constraints.In other words, a democracy puts more emphasis on fair process and the incorporation of competing values than on achieving any particular objective.Thus, although government ownership might solve open-access natural resource problems such as those that occur in fisheries insofar as it creates a peaceable forum for dispute resolution, it does not lead to what anyone might consider well-managed fisheries.For government ownership and well-managed fisheries to coexist, the most logical solution is to create a subset of government structures, the goals of which are aligned with the preferences of various interest groups such as commercial fishers, recreational fishers, and marine conservationists.This approach, which is used on U.S. public lands, ensures that, within at least some parts of the public domain, groups will view management as having succeeded.Greater interest-group satisfaction should lead to welfare gains because those groups will, for example, feel less need to expend resources participating in costly agency processes.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.085 |
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".