The disjuncture of learning and recognition: credential assessment from the standpoint of Chinese immigrant engineers in Canada
Bibliographic record
Abstract
To better recognise foreign qualifications, many OECD countries have promoted liberal fairness epitomised by universal standards and institutional efficiency. This paper departs from such a managerial orientation towards recognition. Building on recognitive justice, it proposes an alternative anchoring point for recognition practices: the standpoint or everyday experiences of immigrants. This approach is illustrated with a qualitative study of the credential recognition practices of the engineering profession in Canada. From the standpoint of Chinese immigrants, the study identifies a disjuncture between credential recognition practices and immigrants’ career stage post-migration. Taking this disjuncture as problematic, it further pinpoints recognition issues such as redundancy and arbitrariness, a narrow focus on undergraduate education, and a deficit view of training from other countries. While some of these issues may be addressed by improving administrative procedures, others demand a participatory space allowing immigrants to become partners of assessment, rather than merely its objects. (DIPF/Orig.)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.021 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".