Stratification in Countries with Flatter (Institutional) Hierarchies? Insights from Administrative Data in Canada
Bibliographic record
Abstract
Researchers have repeatedly found that within modern higher education systems, students from wealthier backgrounds tend to be concentrated in the most advantageous sectors. Dubbed “effectively maintained inequality,” this process allows these groups to maintain a competitive advantage in the labor market by virtue of acquiring more elite credentials. But what happens in nations with flatter university hierarchies, where there is relatively modest vertical differentiation in the brand strength of domestic universities? Through this study, we provide the first national-level analysis of the relationship between parental income and access to more selective, better resourced, and higher ranking Canadian universities. We also assess the extent to which there is an earnings premium associated with attending these more elite institutions. Our results suggest there are few differences in the types of universities attended by Canadians from different economic strata. Moreover, any earnings premium associated with attending a more elite Canadian university disappears once we account for basic demographic and field of study controls. We theorize that Canadian universities’ flatter institutional hierarchy drives wealthy families to seek advantages through enrollment in elite majors (e.g., business, engineering) and other tactics that take place outside the higher education system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".