Between Attrition and Acquisition: the Dynamics between Two Languages in Adult Migrants
Bibliographic record
Abstract
In the context of language use of Romanian migrants living in Canada, this thesis examines the linguistic changes and the contributing social, personal, and affective factors involved in the maintenance of a migrant’s first language (L1) as she or he simultaneously acquires a second language (L2) in an L2-dominant environment. Specifically, it investigates the incidence of L1 attrition in the bilingual group, the existence of a possible systematic relationship between the levels of L1 and L2 proficiency, and the influence of sociolinguistic and cognitive factors on L1 attrition and L2 acquisition in bilingual contexts. L1 attrition is defined here as both the reduction of L1 language skills and the restructuring of the L1 according to L2 patterns to the point where communication in the L1 is impaired. The present study involves extensive elicitation of written and oral data in both Romanian and English. First, an assessment of the participants’ history of language acquisition, their attitudes, their patterns of language use, and the nature of their social networks was conducted via sociolinguistic questionnaires and individual interviews. The language aptitude tests used in this project examined phonetic memory, vocabulary learning, and grammatical inferencing abilities. As an indicator of overall language proficiency, formal tests (a C-test and a verbal fluency task) and samples of spontaneous speech in the form of film commenting tasks were analyzed for fluency and grammatical complexity. The results showed that L1 attrition is a possible, but not obligatory, outcome in the first generation of migrants. With the exception of two participants, the majority of the bilinguals in this study, while not perfect matches for the L1 monolinguals, remained within the L1 native range even after a long residence in the L2 country. While proficiency trade-off patterns are not observable at the group level, they were evidenced in the cases of the two attriters identified in this study. With respect to cognitive predictor variables, L1 maintenance does not appear to be a direct function of linguistic aptitude. At the same time, levels of L1 attrition and L2 achievement are dependent on a combination of attitudinal and personal background variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".