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Record W2947603329

Nous sommes des histoires : Réflexions sur la littérature autochtone

2018· book· fr· W2947603329 on OpenAlexaboutno aff
Louis-Karl Picard-Sioui, Jean‐Pierre Pelletier, Jonathan Lamy

Bibliographic record

VenueMémoire d'encrier eBooks · 2018
Typebook
Languagefr
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Cette anthologie est une plongee dans la culture et dans l’imaginaire des Premieres Nations, des Metis et des Inuits. C’est aussi l’esquisse d’une pensee autochtone par les Autochtones. Pour un vivre-ensemble, pour echanger et etablir la relation, commencons par decouvrir la profondeur de ces histoires et de ces univers. Anthologie dirigee par Marie-Helene Jeannotte, Jonathan Lamy et Isabelle St-Amand Traduction de Jean-Pierre Pelletier Preface de Louis-Karl Picard-Sioui Resume Cette anthologie rassemble des points de vue d’ecrivain.e.s et des textes theoriques. A la fois personnels et engages, ces ecrits montrent la richesse et la fecondite de la pensee autochtone. En plus de fournir des clefs pour la lecture et l’enseignement des litteratures des Premieres Nations, des Metis et des Inuits, ce livre permet de mieux comprendre les enjeux lies a leurs territoires, leurs cultures et leurs imaginaires. Ces voix invitent a penser le monde a partir des histoires qui nous fondent. Extrait de la preface de Louis-Karl Picard-Sioui « S’il y a une chose dont je suis certain, c’est que ce livre est un incontournable pour toute personne – autochtone ou allochtone – voulant se lancer dans l’etude des litteratures autochtones... Ces pages sont riches en experiences concretes, en theories et reflexions ethiques, critiques et philosophiques, et en precieux conseils pour tout chercheur s’approchant, trop souvent de facon bien naive, de ce champ mine. A leur facon, ces textes racontent aussi des histoires. Des histoires d’humains qui, au creux de la nuit, tirent a bout portant des mots-fleches contre le grand mensonge colonial. Des histoires nous invitant a desapprendre pour mieux savoir. » Auteur.e.s Jeannette Armstrong, Thomas King, Lee Maracle, Gerald Vizenor, Drew Hayden Taylor, Sherman Alexie, Neal McLeod, Daniel Heath Justice, Renate Eigenbrod, Sam McKegney, Tomson Highway, Jo-Ann Episkenew, Emma LaRocque, Keavy Martin et Warren Cariou.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.313
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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