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Record W2134570930 · doi:10.1017/s027226310808011x

<b>RAISING BILINGUAL-BILITERATE CHILDREN IN MONOLINGUAL CULTURES</b>

2008· article· en· W2134570930 on OpenAlexaboutno aff
Kendall A. King

Bibliographic record

VenueStudies in Second Language Acquisition · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPatienceWifeRaising (metalworking)First languagePsychologyLinguisticsFrenchNeuroscience of multilingualismSociologyHistoryPolitical scienceSocial psychologyLawEngineering

Abstract

fetched live from OpenAlex

RAISING BILINGUAL-BILITERATE CHILDREN IN MONOLINGUAL CULTURES.Stephen J. Caldas. Clevedon, UK: Multilingual Matters, 2006. Pp. xvi + 231. $39.95 paper. Caldas's work tells the story of his three children's language development over the course of 19 years. Caldas is a native English speaker from Louisiana and a fluent but nonnative speaker of French. Caldas's wife hails from Quebec and is a native French speaker, also fluent in English. Their three children—a boy and twin girls who were born 2 years later—were raised in suburban Louisiana with extended French-immersion vacations in Quebec. Caldas and his wife attempt to make their home a French-only environment by adopting a family language policy of speaking only French themselves and by promoting French-language books, television, and media. Whereas they are successful throughout the children's early years, their project meets resistance when their eldest boy reaches about 10 and begins to reject everything French. The younger girls soon follow in his footsteps. With patience, perseverance, and regular extended visits to French-speaking Quebec, this phase passes, and by the volume's happy ending, all three children become bilingual and biliterate teens (and presumably adults).

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.052
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0520.011

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 head0.047
GPT teacher head0.401
Teacher spread0.354 · 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 designObservational
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

Citations2
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueStudies in Second Language AcquisitionSame topicFrench Language Learning MethodsFrench-language works237,207