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Record W9706265

Maintaining Brazilian Portuguese as a Heritage Language in a Bilingual French - English Environment

2014· dissertation· en· W9706265 on OpenAlexaboutno aff
Andreza Valença da Silva

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPortugueseHeritage languageFirst languageBrazilian PortuguesePsychologyWifeGeographyPolitical scienceMedicineLinguisticsPedagogy
DOInot available

Abstract

fetched live from OpenAlex

This study investigated thirty-six native speakers of Brazilian Portuguese – eighteen husband-and-wife couples –, with children between the ages of 1 and 13 years old, who have arrived in Montreal within the last ten years. The aim of the study was to find out these participants’ attitudes toward the maintenance of their heritage language, Brazilian Portuguese, their reasons for, and the strategies that were used for maintaining Brazilian Portuguese in Montreal, and whether there is a relation between child-raising styles and the maintenance of Brazilian Portuguese as a heritage language. Data were collected via a questionnaire given to each participant individually. Analyses revealed that participants find it important that their children maintain the family language and that they have been successful in maintaining their mother tongue inside their homes. The home is the most important strategy in helping these Brazilian parents’ children to learn Brazilian Portuguese. My research project shows, along with other things, that some of these parents’ disciplinary strategies could contribute to the preservation of their heritage language.

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: none
Teacher disagreement score0.158
Threshold uncertainty score0.314

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.392
Teacher spread0.374 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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