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Record W2767402251 · doi:10.4000/books.pum.3199

Se dire arabe au Canada

2016· book· fr· W2767402251 on OpenAlexaboutno aff
Houda Asal

Bibliographic record

VenuePresses de l’Université de Montréal eBooks · 2016
Typebook
Languagefr
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Toute personne originaire du Machrek, de la région historique de la Grande Syrie ou du mont Liban, a une histoire migratoire à raconter. Dès la fin du XIXe siècle, ils ont été nombreux à quitter leur pays natal pour s’installer partout dans le monde – notamment au Canada –, et jusqu’à maintenant cette chaîne n’a jamais réellement été rompue. La population arabe d’ici est issue de cette histoire, de ces strates d’immigration qui se sont superposées, de ces générations qui se sont croisées et se sont parfois unies dans une volonté de préserver leur patrimoine, de s’entraider ou de se défendre contre la discrimination. Si les « Arabes » sont aujourd’hui l’objet d’une grande attention, aussi bien des médias, des États que des recherches sociologiques, leur histoire reste cependant peu connue. Des origines de leur migration à la fin des années 1970, ce livre fait renaître la voix de ceux qui ont choisi de se faire entendre, de s’organiser et d’exister collectivement sur la scène publique canadienne. Quelles sont les institutions que ces migrants et leurs descendants ont créées et comment ont-ils exprimé leur identité et organisé leur vie religieuse, sociale et politique ? C’est ce que révèle cet ouvrage admirablement documenté.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.043
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0140.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0430.004

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 head0.011
GPT teacher head0.191
Teacher spread0.179 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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