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Record W2566697667 · doi:10.3138/jcs.2016.50.1.36

Listening to Writing: Performativity in Strategies Developed by Learning from Indigenous Yukon Discourse, 1968–84

2016· article· en· W2566697667 on OpenAlexvenueaboutno aff
Lynette Hunter

Bibliographic record

VenueJournal of Canadian Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsPerformative utteranceIndigenousPerformativityPoliticsActive listeningSociologyContext (archaeology)State (computer science)Value (mathematics)AestheticsAnthropologyGender studiesPedagogyLinguisticsHistoryLawPolitical scienceArchaeologyArt

Abstract

fetched live from OpenAlex

The central two stories about anthropologist Julie Cruikshank and community-worker-turned-consultant John Hoyt tell of culturally “Western” people, who are chosen by Native peoples in the Yukon Territory to work on the writing of political stories during the period from 1968 to 1984. This historical period, coming only eight years after Native peoples had been declared “persons,” required a rethinking of the communicative and performative strategies previously assumed by non-Native interlocutors. Each writer had to learn to listen differently, generating in their writing not the concepts of the other at the heart of assumptive logics in the anthropological and Western political discourse of the time, but alterior and not-said values that live alongside what Peter Kulchyski calls a “certain kind of writing that is the state.” Each writer does this differently, addressing in Cruikshank’s case the collaborative work of alongside values emerging into socio-cultural discourse, and in Hoyt’s case the collaborative work of setting alongside values in a context that claims value within state politics. Learning about listening to alterior lives is a key skill that has to be relearned by each generation not as predictive strategies but as ways of becoming and ways of knowing.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.495
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.000
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 head0.038
GPT teacher head0.341
Teacher spread0.303 · 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 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

Citations2
Published2016
Admission routes2
Has abstractyes

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