Scarce Common Flow Resources: Who Benefit? Who Does Society Want to Benefit?
Bibliographic record
Abstract
"This is a theoretical, conceptual contribution related to fisheries. Many common properties around the world have become scarce and potentially valuable because of increased population, and improved technologies: water, forests, grazing lands, waterfowl, mammals, reptiles, fisheries, radio and TV spectrum, geostationary satellite positions, airport take-off and landing slots, air-we-breathe, the gene-pool, etc. Who is going to benefit form these common resources? The scarce common resources cannot be valuable unless one has title to them -- title over their entire range during their life. After establishing jurisdiction and title there is political decision or consensus as to who benefits from these scarce common resources. This is followed by legislative and executive decisions to set up and operate institutions to carry out political decision or consensus as to who benefits. These common resources can be classified according to use: (1) required for sustaining life; (2) contingency for later unspecified use' (3) recreation; and (4) commercial. This allocation will change over time as population and technologies change. One political decision: Is allocation done once for all time or is it continuous over time? What are the problems and consequences?"
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".