The impact of an accent: the experiences of international college students from India in the greater Toronto area
Bibliographic record
Abstract
India is one of the world’s largest sources of international students with 553,440 studying abroad globally, its Ministry of External Affairs estimated in late 2017 (Vanderklippe, 2019). Canada has become an increasingly attractive destination for this cohort of international students. With English as the dominant global language for commerce and politics, and the fact that more people now use English as a second language than a first language (Crystal, 2003), these speakers become uniquely positioned in an English-dominated environment such as Canada, due to their accents. This exploratory study investigates the experiences of Indian international students studying at colleges in the Greater Toronto Area (“GTA”) in relation to speaking with a foreign accent. The primary data was collected through five interviews. This study is enlightened by Pierre Bourdieu’s concept of capital, specifically linguistic capital, which is employed in order to make sense of and understand the participants’ experiences across varying social fields. Keywords: international students; Indian international students; college; Toronto; Canada; foreign accent; perceptions; experiences, cultural capital
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".