Bibliographic record
Abstract
Measuring the Value of a Postsecondary Education is an insightful collection of essays that respond to current and pressing questions in the field of higher education: What do we mean by "quality" of education? What do courses and programs promise to deliver, and do they succeed? What do we know about improving learning outcomes, and is reform possible? Comprised of papers presented at a conference of experts convened by the Higher Education Quality Council of Ontario in 2011, the book begins by evaluating pioneering initiatives in Europe, and follows this with reports on efforts to measure and evaluate learning outcomes. Drawing on over two decades of work by international agencies, governments, and foundations in identifying and evaluating learning outcomes in higher education, Measuring the Value of a Postsecondary Education encourages educational institutions to draw on this evidence in revising course and program offerings. Bringing together international leaders and innovators in the field, this book is an important analysis of progress in enhancing learning quality and directions for future reform. Contributors include Jeana Abromeit (Alverno College), Roger Benjamin (Council for Aid to Education), Ken Dryden (Canadian politician), Michael Gallagher (Group of Eight), Virginia Hatchette (Postsecondary Education Quality Assessment Board), Jillian Kinzie (Indiana University), Diane Lalancette (Organisation for Economic Co-operation and Development), Holiday Hart McKiernan (Lumina Foundation), Robert Wagenaar (University of Groningen), and Lorne A. Whitehead (University of British Columbia).
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 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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".