The Pursuit of Postsecondary Education: A Comparison of First Nations, African, Asian, and European Canadian Youth<sup>*</sup>
Bibliographic record
Abstract
Utilisant l'Enquête auprès des jeunes en transition (EJET), sondage longitudinal nationalement représentatif, l'auteur examine l'argument voulant que les résultats éducationnels inférieurs de diverses minorités visibles et d'immigrants seraient attribués à leurs désavantages socioéconomiques, tandis que les résultats supérieurs des autres minorités visibles auraient pour cause leur soutien culturel. Les analyses rapportent des inégalités non négligeables dans le parcours pédagogique des Premières nations, des minorités visibles et des immigrants. Cependant, ni leur emplacement structurel ni leurs attributs culturels (ni les deux ensemble) n'expliquent entièrement les différences de leur parcours pédagogique ni ne peuvent être réduits à un simple modèle dans lequel les désavantages structurels détermineraient les résultats inférieurs et les facteurs culturels les supérieurs. Using the nationally representative longitudinal Youth in Transition Survey, this paper examines the argument that inferior educational outcomes of various visible minorities and immigrants can be attributed to their socio‐economic disadvantages, while superior outcomes of other visible minorities is due to their cultural supports. The analyses document sizeable inequalities in educational pathways of First Nations, visible minorities, and immigrants. However, neither structural location nor cultural attributes (nor both in conjunction) totally account for differences in their educational pathways nor can they be reduced to a simple pattern whereby structural disadvantages account for inferior pathways and cultural factors for superior ones.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".