Entry Points to US Education: Accessing the Next Wave of Growth
Bibliographic record
Abstract
Entry Points to US Education: Accessing the Next Wave of Growth focuses on the imperative need to modernize international education as a result of the changes in international student mobility. Centered around the ten entry points, the book looks into the distinct preferences and approaches of Generation Z (Gen Z) students, offering data-driven strategies to navigate the ten entry points to U.S. undergraduate degrees. This book also provides actionable strategies and model practices and encourages a national dialogue around student engagement to enhance (in the context of) global mobility. Editors Jing Luan is Provost Emeritus of San Mateo Colleges of Silicon Valley (San Mateo County Community College District) and former President of the Association of International Enrollment Management. Leilt Habte is the Associate Director of the Transfer Center at the University of California Berkeley Center for Educational Partnership. David L. Di Maria is a Senior International Officer and Associate Vice Provost for international education at the University of Maryland, Baltimore County. Krishna Bista is a Professor of Higher Education in the Department of Advanced Studies, Leadership and Policy at Morgan State University, Baltimore, Maryland.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".