Social, Political and Institutional Influences on
Bibliographic record
Abstract
This publication originated in discussions among doctoral students at Indiana University who were concerned about current issues and trends confronting prospective college students and college admissions and enrollment staff. Our earlier study of high school students ’ college-choice process revealed varied approaches to decision making and complex interactions among institutions and prospective students. As we searched the literature for accounts of the college-choice process, we grew increasingly curious about continuity and change in that process: How had the expansion of access to higher education altered students ’ college choice? When did college admissions work become professionalized? We are grateful for the opportunity to explore these questions and to produce this publication. The authors wish to acknowledge and thank Lumina Foundation for Education for its financial support of this project. We would also like to thank the staff at the College Board archives for giving us access to documents and for providing technical assistance. Thanks also go to the staff at the National Association for College Admissions Counseling archives for endless photocopying. Two doctoral students at Indiana University, JoAnne C. Bunnage and Colin Davis, also provided assistance with this research. Harold Wechsler, professor of educational leadership at the University of Rochester, and Joseph D. Creech,
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".