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Record W6925369409 · doi:10.17895/ices.pub.25637028.v1

Getting To Yes With Stakeholders In Fisheries Resources Assessment - A Paradigm Shift

2000· other· en· W6925369409 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2000
Typeother
Languageen
FieldSocial Sciences
TopicSociology and Education in Brazil
Canadian institutionsnot available
Fundersnot available
KeywordsParadigm shiftFishingGovernment (linguistics)StakeholderResource (disambiguation)InvertebrateFisheries managementOrder (exchange)

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.511
Threshold uncertainty score0.956

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.5120.001

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.

Opus teacher head0.104
GPT teacher head0.347
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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