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Record W2992263007 · doi:10.56645/jmde.v10i22.388

Bioeconomic Models and the Formative Evaluation of Fisheries-Related Programs

2014· article· en· W2992263007 on OpenAlex
Ian Graham Cahill, Éric Robard

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of MultiDisciplinary Evaluation · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsFisheries and Oceans CanadaNatural Resources CanadaGovernment of CanadaCanadian Forest Service
FundersFisheries and Oceans CanadaGovernment of Canada
KeywordsFormative assessmentBioeconomicsContext (archaeology)Management scienceResource (disambiguation)Environmental resource managementComputer scienceFisheryEngineeringSociologyEconomicsGeography

Abstract

fetched live from OpenAlex

Bioeconomics combines methods from the biological study of living resources, particularly population dynamics, with methods of economic analysis. Most applications have been in program design for resource management. Although formative evaluations often deal with potential improvements to design based on examination of the program at some point in the early or middle period of its life, there has been little interplay between bioeconomic modelling and evaluation of programs in the context of fisheries management programs.

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.

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)0.074
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0740.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.250
GPT teacher head0.434
Teacher spread0.184 · 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