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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".