MétaCan
Menu
Back to cohort
Record W2529044077 · doi:10.7202/1037549ar

Art autochtone : langue, oralité, communication

2016· article· en· W2529044077 on OpenAlexvenueaboutno aff
Louise Vigneault

Bibliographic record

VenueRACAR Revue d art canadienne · 2016
Typearticle
Languageen
FieldComputer Science
TopicCultural Insights and Digital Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsSemioticsPoliticsSociologyMythologyLeverage (statistics)MediationAestheticsMedia studiesAllegoryLinguisticsHistoryArtLawArt historyLiteraturePolitical scienceSocial sciencePhilosophyComputer science

Abstract

fetched live from OpenAlex

Native languages in Canada are closely tied to memory and territory. Their weakening or disappearance, along with the decrease of the oral transmission of knowledge, has therefore directly impacted the set of semiotic and epistemological codes on which cultures rest. Split between their language of origin and the country’s majority languages, and lacking any political or legislative leverage, communities have progressively lost their means to self-fulfillment. In the Canadian linguistic divide, French-speaking Native communities have found themselves doubly marginalized. Today, artistic creations play a crucial role in the renewal of movements focused on transmission, mediation, and dialogue; they make the unspoken visible and open up new possibilities of expression. For the artists, speaking the language and referring to its reality is an act through which the collective imagination is reclaimed and a spatial and historical anchoring is reactivated. The contributors to this journal’s latest and decidedly polemical section examine these realities and lift the veil on certain lines of thought that have been ignored or too quickly discarded.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.073
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0120.012
Scholarly communication0.0130.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.001

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 head0.100
GPT teacher head0.258
Teacher spread0.158 · 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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueRACAR Revue d art canadienneSame topicCultural Insights and Digital ImpactsFrench-language works237,207