Deconstructing the Environment: The Case of Adult Immigrants to Canada Learning English
Bibliographic record
Abstract
This article identifies and deconstructs the ways in which professionally successful adult immigrants to Canada chose to interact with and reshape different environments in order to foster their English learning process. The sample for this study was selected to be representative of the “brain gain” immigration wave to Canada of the last two decades. All 20 participants belong to the same category of highly-educated (17+ years of education), independent immigrants who came to Canada as young adults. The data collection process consisted of a series of three interviews with each participant. The data were analyzed following the principles of the grounded theory method. Several qualitative themes associated with learning English as an adult immigrant in various types of environments in Canada (instructed environments, ‘manipulated’ naturalistic environments, and unaltered naturalistic environments) emerged from the interviews with the participants. The themes are critically explored and special emphasis is laid on the ways in which participants overcame difficulties inherent in the environmental factors that were not readily structured to offer immigrants opportunities to learn and practice English.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.020 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".