MétaCan
Menu
Back to cohort
Record W4408218259 · doi:10.3390/conservation5010013

Proposing Dimensions of an Agroecological Fishery: The Case of a Small-Scale Indigenous-Led Fishery Within Northwest Territories, Canada

2025· article· en· W4408218259 on OpenAlexafffundabout
Charlotte Spring, Jennifer Temmer, Kelly Skinner, Melaine Simba, Lloyd Chicot, Andrew Spring

Bibliographic record

VenueConservation · 2025
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsAssembly of First NationsUniversity of WaterlooWilfrid Laurier University
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsFisheryIndigenousGeographyAgroecologyScale (ratio)FishingFish <Actinopterygii>EcologyBiologyAgricultureArchaeology

Abstract

fetched live from OpenAlex

As fisheries face intersecting ecological and economic crises, small-scale fishers and Indigenous fishing communities have been organising globally to protect their rights. Yet governance of commercial small-scale fisheries in Canada has been dominated by colonial state actors in the interests of both conservation and economic growth. Meanwhile, agroecology has been considered an appropriate framework for reenvisaging and reshaping food systems in Canada’s North. We propose four dimensions of agroecological fishing: governance, knowledge, economies, and socio-cultural values. We apply these to the Ka’a’gee Tu First Nation fishery in the Northwest Territories. We suggest that these agroecological fisheries dimensions, underpinned by Indigenous values and practices of stewardship, offer an alternative paradigm for the conservation of fish, waters, and fishing communities.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0390.031
Scholarly communication0.0100.002
Open science0.0020.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.306
Teacher spread0.282 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueConservationSame topicIndigenous Studies and EcologyFrench-language works237,207