Bibliographic record
Abstract
This paper aims at overviewing Korean fisheries profile with a view on why Korean fisheries need marine ranching program as a new fisheries paradigm - a community-based co-management approach and drawing some policy implications from the advanced experience in fisheries. The aims of community-based co-management approach as a planned approach can be set out under the planning objectives of efficiency and equity. In this context Tongyeong marine ranching program is an important government-sponsored pilot enterprise. Experiences in the advanced fisheries such as Canadian Snow-Crab case suggest that in order to be successful for marine ranching be successful it is necessary that (i) there should be high-value species, (ii) property right should be established, (iii) there should be the reasonable number of participants involved, (iv) the participants and the government should be able to share resource enhancement/management/utilization responsibilities and associated fisheries management expenses. Among these, management cost sharing based on the practical partnership between fishing communities and government is of particular importance because the government must have a full responsibility for all ranching program expenditure unless revenues sufficient to guarantee reasonable income and to share management cost are generated from the ranching business.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".