Difference, deficiency, and devaulation: Tracing the roots of non-recognition of foreign credentials for immigrant professionals in Canada
Bibliographic record
Abstract
Many immigrant professionals experience devaluation and denigration of their prior learning and work experience after arriving in Canada. The roots of nonrecognition can be traced to the following. First, epistemological misperceptions of difference and knowledge lead to a belief that the knowledge of immigrant professionals, particularly those from Third World countries, is deficient,incompatible and inferior, hence invalid. Second, an ontological commitment to positivistic and universal measurement exacerbates the complexity of thisprocess. The juxtaposition of the misconceptions of difference and knowledge with positivism and liberal universalism forms a new head tax to exclude the undesirable and perpetuate oppression in Canada. Résumé Beaucoup de professionnels immigrants voient leurs expériences de travail et leurs connaissances professionnelles dévaluées et dénigrées lors de leur arrivéeau Canada. L’origine de la non-reconnaissance peut être attribuée aux raisons suivantes. Tout d’abord, il existe de fausses perceptions épistémologiques sur la différence et la connaissance conduisant à la conviction que les connaissances des professionnels immigrants, en particulier celles des pays du tiers monde, sont déficientes, incompatibles et inférieures, et donc invalides. Deuxièmement, un engagement ontologique au positiviste et et à la mesure universelle accentue la complexité de ce processus. La juxtaposition des idées fausses de la différence et de la connaissance avec le positivisme et l’universalisme libéral forme une nouvelle forme d’exclusion des «indésirables», perpétuant ainsi l’oppression au Canada.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".