âNo Canadian experienceâ barrier : a participatory approach to examining the barrierâs affect on new immigrants
Bibliographic record
Abstract
New immigrants to Canada, specifically those of non-Western origin, frequently experience the phenomenon of the ‘no Canadian work experience’ employment barrier. This paper is based on information gathered in a focus group comprised of male and female new immigrants with university education and advanced skills and work experience who have been in Canada for less than five years. The focus group revealed respondents did face the ‘no Canadian experience’ barrier. But they actively created strategies to overcome the barrier, which included: researching and doing more preparation for the realities of the Canadian job market prior to arriving in Canada but not simply relying on insufficient information provided from Canadian government, having decent English language abilities and a mild accent, altering their resumes and verbalization of their experiences to fit in with Canadian employer expectations. This paper also found that government and settlement organization current strategies and services were ineffective for highly educated and skilled immigrants and ignored the needs of immigrant women with young children. In conclusion, issues related to intercultural communication need to be considered for smoothing immigrants’ integration into the Canadian workforce.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".