How Race and Immigrant Status Predict College Enrollment in Toronto and New York City
Bibliographic record
Abstract
We examine longitudinal data in New York City and Toronto to compare how race and immigrant status explain enrollment in two and four-year colleges.Both cities have the largest school districts and are their country's largest immigrant-receiving destinations.The diverse immigrant and race composition of these cities, as well as the availability of comparable data sources make such an analysis not only possible but also allow us to examine how these characteristics operate in different contexts.In the case of foreign-born Blacks in Toronto, their likelihood of 4-year college enrollment was around a third greater than native-born Blacks, while in NYC native-born Asians and Latinx had a notable enrollment advantage over their foreign-born counterparts. ObjectivesNew York City and Toronto are both the largest and most diverse cities in their respective countries.In the latest federal censuses, just over half the residents in the City of Toronto were non-White (Statistics Canada, 2016), compared to around 43% of New Yorkers (United States Census Bureau, 2017).As major destinations for immigrants, Toronto and New York provide unique opportunities to examine and compare how both race and immigrant status are associated with education-related outcomes of young people within different country-contexts.Each of these cities has the largest school district in their nation as well, with New York City Department of Education servicing nearly a million students and the combined public districts (which comprise both secular and Catholic in this case) in Toronto servicing around 350,000.While numerous single city or country analyses have documented how race and immigrant status impact educational attainment, our study focuses on how these status traits operate in different contexts, allowing us to examine macro-social features as possible explanations.In this paper, we seek to answer the question, "How are race and immigrant generation associated with college enrollment in NYC and Toronto?" PerspectivesPublished evidence demonstrating the racial inequities in education is prolific, particularly in the US.The spillover from educational inequality into other areas of social mobility and how this varies by race was identified by Jencks (1972) and continues to be explored by researchers across the disciplines of education, sociology, psychology, geography and many other cognate fields.Scores of studies have demonstrated the achievement and attainment gaps between students of different races, so much so that there is a name for the phenomenon: the "racial achievement gap."Much of this research was summarized by Kao and Thompson (2003), confirming that racial disparities occurred in all areas of educational outcomes, including test scores, dropping out, grades, aspirations, tracking, and college attainment and persistence.In
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".