Trust or betrayal: immigrant engineers’ employment-seeking experiences in Canada
Bibliographic record
Abstract
While trust has been empirically studied and emically defined in a number of studies of employment-seeking practices among second/additional language speakers, particularly in the context of gatekeeping encounters 1 (for example, Kerekes 2006; Campbell & Roberts 2007), it is generally presented as important from the point of view of the (prospective) employer. It is the job seeker’s/employee’s responsibility to make a positive impression on the job interviewer or employment supervisor, in great part by proving her/himself to be trustworthy. Far less research has examined the job seeker’s/ employee’s trust: what role does a job seeker’s or employee’s (dis)trust in her/his (prospective) work environment play in the employment experience, and why might this be important? In the case of internationally educated professionals, the majority of whom speak a second or additional language in their employment environment, how do their experiences with work culture in their country of immigration affect their (dis)trust of local professionals in their employment (-seeking) experiences?
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.010 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".