Draft Report: Costs and Earnings Survey, Inshore and Onshore Fishing Sector, SWNB
Bibliographic record
Abstract
Introduction Over ten years ago, the Social Sciences and Humanities Research Council of Canada (SSHRC) established innovative Community-University Research Alliance (CURA) funding that combines both local and academic knowledge to address social, cultural and economic issues confronting Canadian communities. The focus of this funding is capacity building, sharing of information and knowledge, and development of strategies for decision-making and for the enrichment of academic curricula. The Coastal CURA is a five-year project that takes a regional focus on livelihood problems facing coastal communities in the Canadian Maritimes. This CURA is an alliance of First Nations communities, fishermen’s associations, civil society, government, nongovernmental organizations, and university participants from the Maritime Provinces (New Brunswick, Nova Scotia and Prince Edward Island). Specific goals for Coastal CURA include: examining the current effectiveness of coastal resource governance; increasing community capacity to participate in the integrated management of the coast; establishing a maritime network for community-level governance; and contributing to coastal and oceans research innovation and knowledge generation (see www.coastalcura.ca). This paper reports on one project undertaken as part of the Coastal CURA research agenda. It focuses on coastal Southwest New Brunswick (SWNB), including coastal areas of Saint John and Charlotte Counties (see Map 1). According to the 2006 census data of Statistics Canada, Saint John County has a population of 74,621, of which 10,622 lives in small coastal towns. Charlotte County, with no large city center, has a population of 26,898. Much of this population base outside the city of Saint John lives in small coastal towns and villages. The project examined the contribution of the inshore fishing sector (with boats under 45 feet in length) to the economy of such coastal communities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.013 |
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".