Getting To Yes With Stakeholders In Fisheries Resources Assessment - A Paradigm Shift
Bibliographic record
Abstract
No abstracts are to be cited without prior reference to the author.Over the last 10 years, the Invertebrate fisheries of British Columbia on the Pacific Coast of Canada have grown by an order of magnitude. This growth has mainly been the result of exploitation of new species and expansion of fisheries into new areas. There are present direct fisheries on over 40 species of invertebrates and there are another 30 species for which new fisheries are being requested. There has also been an increase in demands on these resources by non-commercial stakeholder groups including First Nations, Sports fishing and aquaculture. Unfortunately this growth came at a time of fiscal restraint and as a result there were no increases in government funding to carry out the resulting increased need for resource assessments. To meet these shortfalls, a paradigm shift occurred in the way resource assessments where carried out. The shift was from a state where the government conducted all assessments to a state where assessment were only carried out in collaboration with stakeholders. This shift has been so complete that at this time ever Invertebrate assessment is conducted through a co-operative program with stakeholders. This paper will outline the different types of collaborative processes that have been negotiated. Discussions will centre on the principles, infrastructure considerations, roles and responsibilities, difficulties, pit falls and the lessons learned.
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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 | 0.111 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.027 | 0.040 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".