MétaCan
Menu
Back to cohort
Record W4293136526 · doi:10.7202/1088909ar

Une appropriation du discours racial : la figure du white trash chez Pierre Vallières et Victor-Lévy Beaulieu

2021· article· fr· W4293136526 on OpenAlexaffvenueabout
Isabelle Kirouac Massicotte

Bibliographic record

VenueArborescences Revue d études françaises · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Tantôt invisibilisée, tantôt ridiculisée, la figure du white trash est néanmoins prégnante et signifiante en Amérique du Nord. Les différentes disciplines du savoir tardent à produire un discours sur cette figure qui appartient pourtant à nos sociétés et qui est source de moquerie depuis l’époque coloniale. Tout indique qu’il y a une difficulté à aborder de front la place du white trash dans la société et le discours politique. Mon hypothèse est que des oeuvres littéraires québécoises ont recours au white trash, sans le nommer mais en empruntant ses traits, comme forme d’appropriation du discours racial afin d’exacerber le statut de colonisé du peuple québécois, condition à dépasser pour s’inscrire dans l’Histoire. Après avoir présenté comment s’applique la figure du white trash au contexte québécois, j’aborderai les principaux traits du white trash, la question de la femme comme complice des systèmes d’oppression et, enfin, le dépassement du stade de « race dégénérée » pour accéder à l’Histoire dans les oeuvres de Pierre Vallières et de Victor-Lévy Beaulieu.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.373

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.010
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.003
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 head0.010
GPT teacher head0.230
Teacher spread0.220 · 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
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

Citations1
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueArborescences Revue d études françaisesSame topicCanadian Identity and HistoryFrench-language works237,207