Ethno-linguistic pathways to degree completion by institutional type in British Columbia
Bibliographic record
Abstract
The following contribution examines the antecedents and correlates of baccalaureate degree completion by students’ ethno-linguistic background in British Columbia, a system characterized by multiple pathways to completion. Employing an administrative longitudinal dataset, we compare how completion rates at research-intensive universities, teaching-intensive universities, and colleges and technical institutions differ by ethno-linguistic background. Because pathways are dependent on a competitive admissions process in which high-school marks are the primary criterion, reverse probability weights generate insight into how completion differs by institutional type within a hierarchically structured post-secondary system. Multi-level binomial logistic regression and Karlson-Holm-Breen non-linear decomposition analyses demonstrate that, on a whole, Korean, Tagalog, and Vietnamese speakers have lower odds of degree completion compared to English-speaking students. Additionally, the greater proportion of South Asian, Korean, European, and Other language speakers first entering college and technical institutions widens their completion gap with English-language speakers. In order to equalize completion rates by institutional type and among ethno-linguistic groups, our study highlights the need for research on why degree completion rates are lower at college and technical institutions and how the push and pull factors contributing to this educational pathway differ by ethno-linguistic background.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".