Cultural Influences on Impression Management: A Focus on Internationally-Educated Engineers
Bibliographic record
Abstract
Internationally-educated immigrants in regulated professions often encounter numerous barriers when striving to find employment commensurate with their skills and experience in Canada. Understanding the Canadian cultural norms and workplace culture is imperative for their success in the employment interview. This research captures employers' perceptions of how cultural influences in the interview impact their evaluations of job applicants, and in turn, their hiring decisions. To expand upon the existing research, the present study sought to explore how cultural differences within the interview context influence Human Resources professionals' perceptions of candidates and in turn, how these perceptions affect Human Resources professionals' evaluations of job applicants. Through the use of the Enhanced Critical Incident Technique, eight Human Resources professionals within the Oil and Gas Industry offered incidents that facilitated, impeded, and could improve the interview performance of Internationally-Educated Engineers. Five overarching themes emerged from the data analysis: (a) industry-specific knowledge requirement, (b) confidence and abilities, (c) personal attributes, (d) job-search process, and (e) communication skills. These results were considered in light of relevant literature, including recommendations for employers and career practitioners to enhance employment outcomes of skilled immigrants and directions for future research.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".