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Record W2980410361

Refracting the State Through Human-Fish Relations: Fishing, Indigenous Legal Orders and Colonialism in North/Western Canada

2018· article· en· W2980410361 on OpenAlexaffabout
Zoe Todd

Bibliographic record

VenueDecolonization: Indigeneity, Education & Society · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsCarleton University
Fundersnot available
KeywordsIndigenousColonialismPoliticsState (computer science)TreatyIdeologyNegotiationReciprocity (cultural anthropology)Political scienceSociologyGeographyLawEthnologyAnthropologyEcology
DOInot available

Abstract

fetched live from OpenAlex

This piece explores how human-fish relations in a) Paulatuuq, NWT in arctic Canada and b) amiskwaciwâskahikan (Edmonton, Alberta, Canada) in Treaty Six Territory act as a ‘micro-site’ where Indigenous peoples have negotiated, and continue to negotiate, concurrent and often contradictory ‘sameness and difference’ vis-a-vis the State and its ideologies about lands, waters and the more-than-human in order to assert and mobilize imperatives of reciprocity, care and tenderness towards fish as more-than-human beings. I put forth a theory of fish ‘refraction’ and dispersion, which is a process through which Indigenous peoples in Paulatuuq and amiskwaciwâskahikan bend and disperse state laws and norms through local relations to fish and waters. Exploring the ways that humans and fish alike work to navigate the complexities and paradoxes of colonialism in Alberta and the Northwest Territories in the past and present, I theorize a fishy and watery form of refraction of state laws, imperatives and colonial paradigms by Indigenous peoples in Canada. In a time of rapid fish decline across the country --which some argued is tied to the global realities of the Sixth Mass Extinction Event-- I argue for the urgency and necessity of centering human-fish relations, alongside other fleshy engagements, in contemporary and future political struggles.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0150.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.365
Teacher spread0.337 · 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 designObservational
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

Citations93
Published2018
Admission routes2
Has abstractyes

Explore more

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