Comparing apples with apples? How ethnolinguistic and immigration status differentiates university admissions in Toronto and Sydney
Bibliographic record
Abstract
Historically, a scarcity of comprehensive longitudinal microdata has affected comparative research on the interplay between self-identified race, immigrant status, and educational attainment. Thus, this study utilizes ethnic capital theory and harmonized data from Toronto, Canada, and Sydney, Australia, to scrutinize the success of ethnolinguistically diverse immigrants in accessing university education. While students from certain East Asian countries enter universities at higher rates in both cities, dissecting the intricacies of ethnic capital's operation proves challenging. Notably, first- and second-generation migrants who speak Chinese, Japanese, or Korean outdo their peers in university admissions by a larger margin in Toronto than in Sydney. However, the shortcomings of the administrative data in Toronto and the survey data in Sydney limit how we can interpret this finding. We postulate expanding existing data collections to enable insightful research on how the educational trajectories of Canadian students compare to those elsewhere with respect to immigration experiences, race, and ethnicity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".