Fleet Concentration in an ITQ Fishery: A Case Study of the Southwest Nova Scotia Mobile Gear Fleet
Bibliographic record
Abstract
"In this paper, we examine what has happened to the mobile gear, or inshore groundfish dragger, fleet in Southwest Nova Scotia after the introduction of individual quotas in 1991. Our main objective is to provide a case study of one instance where an individual quota scheme was introduced and to examine whether concentration of effort has indeed taken place. In the next section, we provide a discussion of the fishery and the background to the implementation of an individual transferable quota system in the fishery. In the third section, we analyze how ownership of the quota has changed over four years. For the first two years of the program, quotas could only be transferred on a temporary (one-year) basis. In the third year, transfers were allowed to be permanent, although temporary transfers were also allowed. In this section, we use standard measures of concentration to see how the effective ownership of quota has changed over time. In the fourth section, we make use of key informant information to analyze a 'true' ownership of the quota and its effect on concentration. As might be expected, information on the 'true' ownership of a quota indicates concentration is higher than what is shown by the standard concentration indices. In either case, however, the evidence is that there has been an increase in concentration of ownership of quota over the four years. The final section contains our conclusions and summary."
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".