Bibliographic record
Abstract
Contents Editorial: Building the Vision – Higher Education and Quality Assurance in East Africa Pammla Petrucka........................................1 Why Measurement Matters: The Learning Outcomes Approach – A Case Study from Canada Sarah Brumwell, Fiona Deller & Alexandra MacFarlane.....................................5 Quality Issues in Kenya’s Higher Education Institutions Rosemary Kagondu & Simmy M. Marwa..............................23 University Students Learning Experiences: Nuanced Voices from Graduate Tracer Study Omar Badiru Egesah & Mary Nyawira Wahome .................................43 Institutional Constraints Affecting Quality Assurance Processes in Tanzania’s Private Universities Samson John Mgaiwa & Johnson Muchunguzi Ishengoma..................................57 Broadening Perceptions and Parameters for Quality Assurance in University Operations in Uganda Frederick Kakembo & Rita Makumbi Barymak..........................69 Student Evaluation of Teaching: Bringing Principles into Practice Geoff Tennant & Tashmin Khamis.........................89 Reflections on an Innovative Mentoring Partnership Facilitators and Inhibitors to Success in Faculty Development Tashmin Khamis & Marilyn Chapman.......................105 Quality Assurance Self-assessment: A Catalyst at Aga Khan University Tashmin Khamis & Khairunnisa Dhamani...........................................125 Assessing the Cognitive Domain through MCQs: Critical to Quality Assurance in Higher Education Khairunnisa Aziz Dhamani & Zeenatkhanu Kanji..................................135
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.243 | 0.126 |
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".