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Record W2887798475

Nègres noirs, Nègres blancs : Race, sexe et politique dans les années 1960 à Montréal

2015· book· fr· W2887798475 on OpenAlexaboutno aff
David Austin

Bibliographic record

VenueLux Editeur eBooks · 2015
Typebook
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanitiesEthnologyHistory
DOInot available

Abstract

fetched live from OpenAlex

Peu de personnes savent que Montreal a deja ete, du moins pour un bref instant, l'epicentre du Black Power et des autres mouvements de la gauche antiraciste et anticolonialiste. Pourtant, en octobre 1968, le Congres des ecrivains noirs a rassemble a l'Universite McGill intellectuels et militants venus d'ailleurs au Canada, des Etats-Unis, des Caraibes et du continent africain. C.L.R. James, Stokely Carmichael, Miriam Makeba, Rocky Jones et Walter Rodney, pour ne nommer que certains des plus connus, ont ainsi inspire nombre de militants quebecois. Quelques mois plus tard, d'ailleurs, un puissant mouvement d'occupation mene par des etudiants noirs s'emparait de l'Universite Sir George Williams. Dans l'atmosphere explosive de l'epoque, il n'en fallait pas plus pour que les medias et les services de securite du pays voient Montreal comme un foyer de la contestation noire dont le discours anticolonialiste avait aussi le potentiel d'enflammer le mouvement pour l'emancipation nationale du peuple quebecois. Meticuleusement documente, Negres noirs, Negres blancs ebranle la vision traditionnelle de l'histoire de l'internationalisme noir et offre une analyse approfondie des enjeux politiques de l'epoque entourant les questions de pouvoir, de genre et de race. Le Canada ? pas plus que le reste du monde ? ne s'est toujours pas libere du racisme. Cet ouvrage eclaire de la lumiere du passe de nouvelles pistes pour arriver a une reelle emancipation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.275
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.006
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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; both teacher heads agree on what is shown here.

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
Published2015
Admission routes1
Has abstractyes

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