I came, but I’m lost: Learning stories of three Chinese international students in Canada
Bibliographic record
Abstract
Abstract The number of international students arriving in Canada is increasing annually, with students from China accounting for the highest number. Grounded in sociocultural theories of second language learning, identity, investment and Community of Practice (CoP), this paper presents selected findings from a narrative study investigating the experiences of Chinese international students preparing for the International English Language Testing System (IELTS) tests in Canada. Based on their own accounts of English learning before and after coming to Vancouver, this paper finds that the participants recognized the capital and power of English and foreign qualifications, and regarded international education as a sanctuary from examinations in China. By comparing their current learning in different settings, they expressed confusion about engaging/disengaging in different communities, and about their past expectations, current experiences as well as future possibilities. This paper hopes to draw the attention of stakeholders (institutions and test issuing organizations) to the more nuanced challenges that international students encounter so that better support can be provided to international students learning English for academic purposes.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.043 | 0.016 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".