A comparative view of the South African and Canadian framework for issuing work visas to skilled refugees and asylum seekers
Bibliographic record
Abstract
South Africa faces a shortage of skilled workers due to long-standing systemic challenges that prevent it from producing the skills necessary for economic development. In 2021, only 25 per cent of persons employed in South Africa were considered highly skilled. The critical skills work visa has been designed to facilitate the employment of skilled immigrants, but is unsuitable for doing so in the case of skilled asylum seekers and refugees, even though the latter could alleviate the shortage of skilled workers. While members of this group are eligible to apply for a critical skills work visa, they face significant obstacles that hinder their chances of obtaining one. This article highlights the barriers this group encounters and draws lessons from Canada's Economic Mobility Pathways Project, which has successfully connected skilled refugees to employers and filled in-demand positions. In South Africa, the likelihood of obtaining a critical skills work visa without governmental intervention is low for many in this group, resulting in a waste of their skills. The article compares the South African case to how Canada has integrated skilled refugees to occupations requiring skills. Canada's partnerships with NPOs such as Talent Beyond Borders have been vital in assisting skilled refugees and connecting them to employers. The article thus argues that to employ skilled refugees in positions commensurate with their skills, the South African government has to assist and form partnerships with organisations specialising in this cause.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.065 | 0.023 |
| Scholarly communication | 0.017 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".