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Record W6959792953 · doi:10.7939/r3-bev6-r519

Bilingual Phonological Development of French Immersion Students

2023· dissertation· en· W6959792953 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2023
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicGenetic and Environmental Crop Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConsonantFrench immersionSpeech productionNeuroscience of multilingualismError analysisContext (archaeology)Focus (optics)Speech errorFirst language

Abstract

fetched live from OpenAlex

The current research of French Immersion suggests that the education of a minority second language is complex, important to study and yet to be understood in the context of Western Canada (Walker, 2012). This thesis aims to better understand bilingual speech productions in French Immersion students in Alberta, by identifying children’s emerging and accurately produced consonants. This thesis will focus on the following research questions: What is the overall percentage of consonants correct across grade levels and what are the patterns of accuracy for each consonant in initial and final position? What characteristics influence accuracy? This will be followed by an additional acoustic analysis of stop consonants that aims to highlight the variability identified in final position. A total of 37 students participated in the study from grades 1, 3 and 5. These students are fluent bilinguals and the conditions in the community require the use of English in the majority of contexts outside of school and family circles. They completed French picture naming tasks that contained targeted consonants and probed spontaneous speech productions. Their speech productions were transcribed and analyzed acoustically. From the transcriptions, the student’s accuracy of consonant production and error patterns were obtained. A descriptive analysis was used to highlight consonant accuracy in grade and word position. Finally, a mixed effects logistic regression model was used to measure the effect of word position, grade and unshared/shared consonants. From the acoustic transcriptions, the characteristics of their final consonants were measured, and a linear mixed effects model was used to analyze the acoustic measurements of stop consonants in word final position paired with a descriptive analysis of acoustic values. The results reveal students in grade 1 were able to produce 90.3% accuracy, students in grade 3 produced 91.0% accuracy and students in grade 5 produced 95.8% accuracy across all consonants. Despite relatively high consonant accuracy, these results reveal accuracy that is lower than existing consonant accuracy in francophone children. Challenges emerged with the nasal palatal /ɲ/, the fricative alveolar /z/ and fricative alveolar /s/, specifically in final position. In addition, de-voicing was a common challenge with the bilabial stop /d/, velar stop /g/. These findings highlight specific developmental data of French Immersion students. In addition, the acoustic analysis documents an undefined grade progression in either duration measures. This study focused on the phonological emergence of French Immersion students learning French in a minority sociolinguistic environment through two spontaneous speech tasks, PCC and acoustic analysis of stop consonants. The results of this study establish reference data for French Immersion students. The descriptions of children's emerging consonants provides insight on their phonetic and phonological systems and can be used to inform researchers, educators and policy makers in French Immersion education programs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.191
Teacher spread0.178 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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