Multilingual International Students at a Canadian University: Portraits of Agency
Bibliographic record
Abstract
This paper draws on the theoretical concepts of individual agency and academic buoyancy to explore the ways in which three multilingual international students responded to and overcame the challenges they encountered while studying at a university in Canada. This investigation is situated within and against the discourses of deficit and disruption that have traditionally surrounded the accounts of international students who use English as an additional language (EAL) in institutions of higher education. Methodologically conceptualized through the lens of portraiture, which focuses on identifying goodness in participants, this paper presents the portraits of the three students with an attention to the physical and sociocultural contexts where the students were embedded. Individual semi-structured interviews and observations of the host institution were conducted in order to gain an in-depth understanding of the students’ experiences. Findings indicate that the students’ challenges were complex, found primarily in the linguistic, academic, and social dimensions, and originating from both structural and individual factors. Simultaneously, the students strategized and employed agency by seeking support in conventional and alternative ways and by refusing to conform to institutional expectations. This paper is concluded with a discussion about the importance of considering the context when examining multilingual international student agency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.025 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| 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".