Linguistic Capital and Academic Achievement of Canadian‐ and Foreign‐Born University Students
Bibliographic record
Abstract
Au Canada, plusieurs universités prennent des mesures pour recruter des immigrants ou leurs enfants et satisfaire leurs besoins—et parmi eux plusieurs ont l'anglais comme langue seconde. Il n'y a pas de recherches au Canada qui comparent la progression potentielle du capital linguistique des étudiants ayant l'anglais comme langue seconde et celui des autres étudiants au fil de leur parcours universitaire, avec les relations entre les progressions du capital linguistique et de l'acquisition des connaissances. L'auteur montre dans cette étude que, contrairement aux étudiants canadiens et ceux nés à l'étranger pour lesquels l'anglais est la première langue, le capital linguistique des étudiants nés à l'étranger dont l'anglais est une langue seconde s'accroît au cours des quatre années d'études universitaires. Cependant, cette augmentation du capital linguistique ne correspond pas à une augmentation de l'acquisition des connaissances. In Canada, many universities are taking steps to recruit and meet the needs of immigrants and/or their sons and daughters, many of whom have English as a second language (ESL). There is, however, no research in Canada comparing potential increases in the linguistic capital of ESL and other students over the course of their university careers and the connection between increases in linguistic capital and academic achievement. In this study, it is shown that in contrast to Canadian‐ and foreign‐born students for whom English is a first language, and Canadian‐born ESL students, the linguistic capital of foreign‐born ESL students increases over 4 years of university study; however, this increase in linguistic capital is not paralleled by an increase in academic achievement.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".