Confronting educational inequality : reframing, building understanding, and making change
Bibliographic record
Abstract
Part I: Reframing William G. Tierney and Kristan Venagas, Cultural Ecology of Financial Aid Decision Penny A. Pasque, Seeing More of the Educational Inequalities Around Us: Visions Toward Strengthening Relationships Between Higher Education and Society Edward P. St. John, Students' Voices and Graduate Choices: The Role of Finances in the Lives of Low-income, High-achieving Students of Color Part II: Building Understanding Laura W. Perna, Michelle Asha Cooper, and Chunyan Li, Improving Educational Opportunities for College Students Who Work Masamichi Inoue and Terry G. Geske, Seeking Educational Opportunity: Desegregation of Higher Education and the TOPS Scholarship in Louisiana Amy S. Fisher, Valuation of Higher Education: An Examination of Possible Explanations in Privatization Ontario S. Wooden, Guidance Counselors as Reproductive Forces in the Lives of African American Students: A Study of a Georgia High School Part III: Making Change Luis Miron, Activist Research, Post Katrina: One Tool for Renewal Kimberley Kline, Professional Development in Student Affairs: From Learning About Diversity to Building Just Communities Edward P. St. John and Shouping Hu, Chapter Reform, Scholarship Guarantees, and College Enrollment: A Study of the Washington State Achievers Program Part IV: Conclusions Edward P. St. John, Contending with Inequality: Lessons Learned.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".