Nontransferability of the skills and qualifications among the Caribbean diaspora in Toronto, Canada
Bibliographic record
Abstract
About 35% of the Canadian labor market is made up of highly skilled immigrants, yet they have the highest unemployment rates. This study filled a gap in knowledge related to reasons Caribbean credentials are not more widely recognized and accepted in Canada and why their skills and qualifications are nontransferable. The purpose of this qualitive research was to identify the barriers to the successful integration of Caribbean immigrants into the Canadian job market and to obtain strategies to remove them. The theoretical framework that underpinned this study was Rawls Social Justice theory. Data were collected through semi structured interviews with 15 participants. Data were analyzed using both manual coding and qualitative analysis software. The results of the study revealed that the barriers to the recognition of Caribbean credentials include (a) lack of awareness of Caribbean universities by employers, (b) lack of Canadian work experience, (c) perception of the quality of the degree, (d) racism and discrimination, and (e) lack of requirements of regulated professions. The strategies identified include raising the profile of Caribbean universities, developing a memorandum of understanding between the two regions at the government level, and the publishing of nondiscriminatory credential evaluation standards, among others. The findings of this study could have implications for positive social change by providing Canadian Immigration officials with guidance on removing barriers to employment for Caribbean immigrants in Canada through suggested modified or enacted immigration and employment policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".