Prior Learning Assessment of Immigrants Competences—a Systematic Review
Bibliographic record
Abstract
Abstract As immigration increases around the globe, the assessment and recognition of prior learning experiences become inevitable to incorporate foreign-trained professionals. However, even though Prior Learning Assessment and Recognition (PLAR) is claimed to be a source of social inclusion, it encourages a dividing practice—potentially building more barriers than bridges. This systematic review analyzes the practice of PLAR in the case of recent immigrants using 39 articles published between 1990 and 2020. The research reviewed was primarily conducted in Canada and Sweden, followed by other European countries. The systematic review synthesizes the context in which PLAR is used, the difficulties encountered during the process, and the impact of the process. By doing so, it pinpoints a new baseline for future innovative research. The analysis focuses on three identified difficulties: (1) language influence, (2) labour market demands, and (3) systemic limitations. The findings raise the question whether PLAR is an appropriate tool for the assessment of immigrants’ prior learning, as the plurality in knowledge and education is not valued during the process. Therefore, overall systemic change is needed to enable social inclusion.
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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 | 0.023 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".