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Record W4223558117 · doi:10.3138/tric.43.1.a01

(K)new Materialisms: Honouring Indigenous Perspectives

2022· article· fr· W4223558117 on OpenAlexaffvenue
Virginie Magnat

Bibliographic record

VenueTheatre Research in Canada · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicPosthumanist Ethics and Activism
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsHumanitiesPhilosophyEthnologySociology

Abstract

fetched live from OpenAlex

Cet article met au premier plan les principes éthiques autochtones de respect, de réciprocité, de relation et de responsabilité qui lient les agents humains aux agents autres-qu’humains ou plus-qu’humains – conception non anthropocentrique de l’agentivité dont on peut dire qu’elle propose une approche écocritique véritablement radicale, à défaut d’être nouvelle, des questions cruciales soulevées par les recherches fondées sur les nouveaux matérialismes et le posthumanisme. Virginie Magnat défend l’idée que la distinction entre cette compréhension de l’agentivité et celle de Karen Barad dans son influente théorie du réalisme agentiel est la non-séparabilité de la matière et de l’esprit postulée par les chercheuses et chercheurs autochtones Vine Deloria Jr, Gregory Cajete, Manulani Aluli Meyer et Shawn Wilson, entre autres. Elle soutient que le fait de faire honneur à la philosophie autochtone, qui valorise les méthodes de savoir incarnées transmises oralement, contribue à décentrer, déstabiliser et décoloniser les paradigmes eurocentristes qui structurent encore les systèmes de connaissance dominants dans le monde universitaire; elle montre que les travaux de Dylan Robinson et de Dolleen Tisawii’ashii Manning, par leur approche critique du matérialisme et du posthumanisme, ouvrent de nombreuses possibilités à un dialogue interculturel plus nécessaire que jamais.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.039
Scholarly communication0.0070.010
Open science0.0010.006
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.090
GPT teacher head0.373
Teacher spread0.283 · 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 designTheoretical or conceptual
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

Citations9
Published2022
Admission routes2
Has abstractyes

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