Honouring the Adult Learner Through Recognition: Critically Exploring Immigrant Experiences of PLAR Considerations in Saskatchewan
Bibliographic record
Abstract
Prior learning assessment and recognition (PLAR) denotes a broad range of activities that make prior learning visible and supports attaining formal recognition for that learning, specifically for adult learners. With immigration increasing worldwide, the need for foreign credential recognition innovation is crucial. While the validation of knowledge, skills, abilities, personal attributes, and competencies is seen as an essential part of the immigrant integration process, using PLAR as part of the assessment and recognition of prior learning experiences of foreign educated professionals has not been an area greatly explored. Critical research has illuminated persistent challenges in the underlying structures of PLAR with some researchers expressing concern about PLAR’s deskilling and exclusionary practices when it comes to assessing immigrants’ prior learning. What seems to be missing are the voices of immigrants using, or considering using, the PLAR process. This research study explores the experiences of immigrants in Saskatchewan who are considering using PLAR at post-secondary institutions in Saskatchewan. Using a narrative inquiry methodological approach, and guided by an integrative intersectionality framework, the experiences of seven participants were storied from interviews and analyzed thematically using existing theories on PLAR. This study amplifies the stories of seven persons seeking to use PLAR to better understand their needs and experiences drawing on interviews. This study found that persistent barriers to recognition and commensurate employment exist for immigrants moving to Saskatchewan including unemployment/underemployment, and negative impacts on mental health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".