Comparative analysis of higher education systems : issues, challenges and dilemmas
Bibliographic record
Abstract
This is a well crafted, timely book that comes at a time when so much is happening in higher education contexts across the world. Clearly, it is in response to these global (and selectively local) trends that Kariwo, Gounko and Nungu bring together an impressive lineup of both established and emerging scholars who achieve a comprehensive and critically constructed perspective on tertiary education systems. Collectively, the chapters in this work shall expand the epistemic boundaries of the area and its affiliated disciplines, and the book as a whole will greatly benefit interested scholars, students, education policy makers and the public at large. - Ali A. Abdi, Professor, University of Alberta This book is a valuable contribution to knowledge on higher education and provides an international perspective on issues, challenges and dilemmas resulting from the rapid expansion of higher education. The volume is an excellent text that integrates theoretical and analytical studies as well as empirical regional studies. The book gives some insights on how different countries and regions have been responding to massification and accessing of higher education. It will appeal to researchers, graduate students and faculty in Higher or Post-Secondary Education as well as International and Comparative Education. - Edward Shizha, Associate Professor, Wilfrid Laurier University (Brantford Campus)
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".