The ‘hesitant multilingual’: why do some students who use multiple languages not identify as multilingual?
Bibliographic record
Abstract
While one prevailing view of multilingualism conceives all learners engaged in additional language learning as multilinguals, these learners may not always self-identify as such. The data in this paper come from a project examining the experience of students with a first language other than English at three Canadian universities. This paper focuses on the subset of respondents who did not identify as bi/multilingual in the initial project questionnaire or didn’t know if they were (n = 27 out of N = 173). This was surprising because students’ participation in the project presupposed knowledge of at least two languages, which we assumed would lead them to self-identify as bi/multilingual. We refer to these participants as ‘hesitant multilinguals’. We report on reasons behind their hesitance to self-identify as bi/multilingual through the analysis of follow-up interviews with some of these students (n = 9). Our findings suggest that for this group of students there were three main reasons that led them not to self-identify as multilingual. These reasons include self-perceptions of insufficient language competence, contextual issues specific to Quebec, and affective issues associated with their status as recent arrivals in Canada. We also report on changes to students’ self-identification as bi/multilingual after a year of university study.
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 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".