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QUEM É ESTUDANTE FALANTE DE PORTUGUÊS EM FAMÍLIAS DE ORIGEM BRASILEIRA EM TORONTO, CANADÁ? QUESTÕES DE CLASSE

2018· article· pt· W2902242028 on OpenAlexaboutno aff
Pedro de Moraes Garcez

Bibliographic record

VenueLinguagem em (Dis)curso · 2018
Typearticle
Languagept
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesSociologyArt

Abstract

fetched live from OpenAlex

Resumo Filhos de famílias de migrantes de origem brasileira em Toronto podem ser vistos como estudantes falantes de português, expressão usada por direções escolares da cidade para identificar luso-canadenses marcados por insucesso escolar. Entrevistas com estudantes de origem brasileira que frequentavam escolas de uma mesma Direção Escolar e suas famílias mostram, porém, perfis socioeconômicos distintos, conforme indicado pelas regiões de residência e as ocupações dos pais, associados a ideologias de linguagem diferentes. Amostras do discurso de entrevistados em cada perfil sobre o valor de falar português revelam indícios de aproximação apenas dos migrantes brasileiros com menos qualificação profissional à etnoclasse falante de português luso-canadense. O distanciamento do português por parte de uma estudante nesse perfil que, entretanto, possui aspirações acadêmicas aprofunda o entendimento das diferentes perspectivas do que é ser estudante falante de português em Toronto. O estudo reforça a relevância de classe social para os estudos da linguagem na contemporaneidade.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.528

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0190.008
Scholarly communication0.0080.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.054
GPT teacher head0.442
Teacher spread0.388 · 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 designQualitative
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

Citations3
Published2018
Admission routes1
Has abstractyes

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