Professional Identities of French Lx Economic Immigrants: Perceptions from a Local French-Speaking Community
Bibliographic record
Abstract
Communicative expertise in the host society’s dominant language is central to newcomers’ socio-professional integration. To date, SLA research has largely ignored laypeople’s perspectives about Lx communicative expertise, though they are the ultimate judges of real-life interactional success. Sociolinguistic studies have shown that laypeople may base their judgments of Lx speech not only on linguistic criteria, but also on extralinguistic factors such as gender and language background. To document laypeople perspectives, we investigated the professional characteristics attributed to four ethnolinguistic groups of French Lx economic immigrants (Spanish, Chinese, English and Farsi) who were nearing completion of the government-funded French language training program in Quebec City, Canada. We asked L1 naïve listeners (N = 49) to evaluate spontaneous speech excerpts, similar in terms of content and speech qualities, produced by a man and a woman from each target group. After they listened to each audio excerpt, we asked listeners to select the characteristics they associated with that person from a list of the most frequent professional qualities found in job advertisements. Data analysis showed that few Lx users were perceived as having strong communication skills in French. Logistic regression revealed no significant relationships between language group, gender, communicative effectiveness, and professional characteristics. However, there were significant associations between communicative effectiveness with the following characteristics: can work independently, can relate to others, is dynamic, has a sense of initiative, and shows leadership skills.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".