Bibliographic record
Abstract
In this article, I describe the adjustment experiences of 23 Chinese-speaking, foreign-born high-school students in Vancouver, where they are members of the largest school cultural group. During interviews, participants suggested their large numbers enabled them to reproduce their home community in Canada. Students chose behaviours that hindered their adjustment and adaptation. They did not form Canadian friendships, learn English easily, make cross-cultural adaptations, or excel in school. Students’ responses raise questions about the direction of Canadian education, the evolution of Canada’s pluralistic society, and the obligation of the education system to familiarize students with Canadian democratic life. L’étude porte sur les problèmes d’adaptation de 23 élèves sinophones, nés à l’étranger et fréquentant une école secondaire de Vancouver. Invoquant le fait qu’ils constituent le groupe culturel le plus important, ils se disent en droit de reproduire au Canada leur communauté d’origine et refusent de s’intégrer, soit socialement, soit en apprenant l’anglais. Leurs réponses suscitent des questions au sujet de l’orientation de l’enseignement, de l’évolution du pluralisme et de l’obligation du système d’éducation de familiariser les élèves avec la vie démocratique canadienne
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.046 | 0.014 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".