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Record W4392974235 · doi:10.1093/icesjms/fsae026

Learning, thinking, sharing, and working across boundaries in fisheries science

2024· article· en· W4392974235 on OpenAlexafffund
Steven J. Cooke, Robert Arlinghaus

Bibliographic record

VenueICES Journal of Marine Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFisheryFisheries scienceGeographyFisheries managementBiologyFishing

Abstract

fetched live from OpenAlex

Abstract Fisheries science and practice are challenging and require learning, thinking, and sharing across boundaries. The idea of boundary crossing can be described as some form of multiple disciplinarity (e.g. interdisciplinarity, transdisciplinarity), yet that inherently implies that the boundaries crossed are purely disciplinary in nature. After working across various other boundaries (i.e. realms, regions, disciplines, sectors, domains, and knowledge systems) for most of our educational journeys and professional careers, we reflect on our lived experiences with a focus on identifying the benefits and challenges of engaging in different types of boundary crossing. We submit that our personal and professional lives have been enriched by stepping outside of our immediate comfort zones and expertise (i.e. fish ecology) and engaging in active listening and learning with colleagues in other disciplines (i.e. various social sciences) and with stakeholders and policymakers. We have learned much from working across boundaries and encourage others, especially early career professionals, to do the same. What may superficially appear to be a bridge too far may in fact provide novel ways of thinking about a given issue or topic that generates actionable science for sustainable fisheries management and conservation. Many of the projects that we consider to be our greatest successes represent ones that involved boundary crossing, examples of which we provide in this essay. There is a need to prepare the next generation of problem solvers for engaging in boundary crossing and celebrating examples of where such efforts have led to meaningful advances in fisheries science and practice. Ensuring that institutional and cultural barriers that may constrain boundary crossing are addressed while also supporting those doing such work will be key to address the many fisheries and aquatic science challenges of today and tomorrow in both marine and freshwater systems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.028
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0210.055
Scholarly communication0.0170.023
Open science0.0030.021
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.264
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations8
Published2024
Admission routes2
Has abstractyes

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