A New Measuring Stick Is Access to Higher Education in Canada Equitable?
Bibliographic record
Abstract
organization dedicated to policy-based research on educational opportunity for all students. With offices in Washington, DC, and Toronto, ON, EPI is a collective association of researchers and policy analysts from around the world dedicated to the mission of enhancing our knowledge of critical barriers facing students and families throughout the educational pipeline. In addition, EPI has developed extensive partnerships and collaborative arrangements with other leading research and educational organizations, further supporting our mission and ability to conduct policy-relevant research for practical use. The mission of EPI is to expand educational opportunity for low-income and other historicallyunderrepresented students through high-level research and analysis. By providing educational leaders and policymakers with the information required to make prudent programmatic and policy decisions, we believe that the doors of opportunity can be further opened for all students, resulting in an increase in the number of students prepared for, enrolled in, and completing postsecondary education. epi For more information about the Educational Policy Institute, please visit our website at: www.educationalpolicy.org or contact us at:
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".