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Record W2521807837 · doi:10.18584/iipj.2016.7.3.4

A Mi’kmaw Perspective on Advancing Salmon Governance in Nova Scotia, Canada: Setting the Stage for Collaborative Co-Existence

2016· article· en· W2521807837 on OpenAlexafffundvenueabout
Shelley Denny, Lucia Fanning

Bibliographic record

VenueInternational Indigenous Policy Journal · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNova scotiaIndigenousContext (archaeology)Corporate governanceSalmoCollaborative governancePerspective (graphical)FisheryPolitical scienceIdentity (music)Process (computing)SociologyGeographyEnvironmental resource managementEnvironmental planningFish <Actinopterygii>EthnologyManagementEcologyEconomicsBiology

Abstract

fetched live from OpenAlex

Issues related to fisheries governance are a source of debate and tension between the Indigenous Mi’kmaq people of Nova Scotia and the Canadian Department of Fisheries and Oceans (DFO) in matters concerning Atlantic salmon, Salmo salar. Within the context of the existing governance regime, this analysis compares the concept of salmon conservation and management from a Mi’kmaq perspective and proposes a collaborative co-existence approach for effective salmon governance in Nova Scotia. This approach begins by using co-management as a process, Two-Eyed Seeing as the design, and treaties as the model to achieve shared objectives of maintaining and improving abundances of salmon populations, in spite of differing mechanisms for addressing the interwoven complexities of multiple realities, conservation, and cultural identity.

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)0.003
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 score0.092
Threshold uncertainty score0.669

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.010
Scholarly communication0.0050.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.413
Teacher spread0.380 · 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

Citations79
Published2016
Admission routes4
Has abstractyes

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