Chinese Students in Canadian Higher Education: A Case for Reining in Our Use of the Term “Generation 1.5”
Bibliographic record
Abstract
Roberge defines the 1.5 Generation as “those who immigrate as young children and have life experiences that span two or more countries, cultures and languages” (2009, p. 4). In US and Canadian higher education, the term has gained considerable recognition, with the scope of the term broadening among some educators to include bi/multilingual students in general. In this article, we present selected data on students of Chinese ethnicity (322 survey respondents and three interviewees) from a broader two-year study of the languages, literacies, and identities of multilingual undergraduate students in Vancouver, where, in the 2011 census, one in five of the people living in the city reported being of Chinese ethnicity (Statistics Canada, 2011). Our aim was to analyze how key social, cultural, and linguistic defining features of the term Generation 1.5 that we found in the literature were represented in participants’ survey and interview responses to open questions about their languages and identities. Five themes emerged: (a) being foreign-born and finishing secondary school in Canada, (b) being an international student, (c) being somewhere in between here and there, (d) (in)competence and language use, and (e) perceiving deficit in cultural knowledge. Participants’ responses illustrated complex, transnational interweavings of languages, identities, and literacies around these five themes, leading us to question our institutional use of the homogeneous term Generation 1.5 to describe a heterogeneous group of multilingual, transnational students of Chinese ethnicity.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.049 | 0.017 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".