Effectively Maintained Inequality in Canada Revisited
Bibliographic record
Abstract
The mass expansion of higher education (HE) systems during the 20th century pushed social scientists to theorize how high participation systems continued to reproduce inequalities across socio-economic lines. One popular theory in sociology, dubbed effectively maintained inequality (EMI), suggests that families from the upper economic strata would maintain their competitive advantage by not only acquiring increasing amounts of education, but also gravitating toward the most prestigious tracks within HE. Despite the "flatter" status structure of Canada's HE system vis-à-vis international counterparts, this is a theory that has received empirical support from several domestic studies. Through this study, we re-examine the EMI hypothesis using the 2005 Ontario University Applicant Survey (OUAS), a little-known and thus far unexamined dataset that offers notable advantages relative to those historically analyzed in the Canadian EMI literature, including representative coverage of applicants to Ontario universities, holistic coverage of academic and demographic controls, and the ability to analyze both within- and between-sector forms of status-seeking. Our statistical analyses suggest that applicants from privileged socio-economic backgrounds behave in ways consistent with EMI, gravitating towards more prestigious HE options. We conclude by sketching a path forward for social stratification research in Canadian HE.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".