Developing a method for profiling Danish fishing communities for impact assessments of changes in fisheries policies.
Bibliographic record
Abstract
This article presents a method for profiling fishing communities in Denmark. Creating a database of community profiles can provide a foundation assessing the potential impacts of proposed fisheries policies. The approach builds on recent research into social indicators in fisheries and the profiling of fishing communities, often within the context of ecosystem-based management and impact assessments. A fishing community is defined as place-based—typically a port or landing site with surrounding built infrastructure and a minimum threshold of value of commercial landings. To evaluate socio-economic reliance on fisheries, the method includes both the fishing industry and directly related supply sectors, as well as other local economic activities. The socio-cultural dimension of fishing reliance and potential community resilience is addressed by incorporating both formal and informal institutions into the profile. The profiling method comprises 19 indicators and identifies both quantitative and qualitative data sources. It has been tested in one community with promising results. Further testing in other types of communities is recommended before establishing a national database of Danish fishing community profiles.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".