Strategies for Sustainable Open and Distance Learning
Bibliographic record
Abstract
1. From Policy to Implementation VIS NAIDOO Study: The of Mozambique Arnaldo Valente Nhavoto Study: Educational Broadcasting: The Indian experience Usha Vyasulu Reddi 2. Planning for Sustainability Geoff Peters Case Study: BOCODOL: Its background and the educational context Daniel R. Tau Case: Study: NAMCOL: Its background and educational context Frances J. Mensah 3. Managing Change for Sustainability Bruce King Case Study: Ghana's Distance Education Programme Joshua Caleb Mallet Canadian Study: Distance Education in a Dual-Mode Higher Education Institution A.W. (Tony) Bates Case Study: Moving to Flexible Delivery at the University of South Australia (UNISA) Bruce King 4. Teaching, Learning and Student Support Roger Mills Case Study: Studies in the Introduction of Technology Mediated Learning in ODL Francois Marchessou Case Study: Commonwealth Diploma in Youth Development: The UNISA case study Evelyn Nonyongo Case Study: A Study of Learner Support Developments in the Botswana College of Distance and Open Learning Daniel R. Tau 5. The Management of Systems in Open and Distance Learning Patrick Guiton Case Study: Student Record Systems and Learner-centred Management David Sewart Case Study: Distribution of Materials for an In-service Teacher Training Distance Education Course in Mozambique Lurdes Patrocinia M. Nakala Case Study: Professional Development Helen Lentell and Christine Randell 6. Strategic Alliances - Collaboration for Sustainability Sally M. Johnstone and Sharmila Basu Conger Case Study: The Online Master of Distance Education and Certificate in Distance Education Programmes Jointly Offered by the University of Maryland University College and Carl von Ossietzky University of Oldenburg Ulrich Bernath Case Study: A Joint Degree Programme between Regis University, Denver, Colorado, USA and the University of Ireland at Galway, Republic of Ireland William J. Husson Case Study: International Online Master's Degree Programme in Drug Abuse Control and Prevention Antonio Lomba Maurandi and Maria Eugenia Perez de Madrigal 7. Quality Matters: Strategies for ensuring sustainable quality in the implementation of ODL Andrea Hope Case Study: Implementing Quality Systems V.S. Prasad Case Study: Quality Standards for Consumer Protection Kathryn Chang Barker 8. Counting the Cost Hilary Perraton Case Study: Funding and Financial Management at the Indira Gandhi National Open University C. Gajendra Naidu Editor's Conclusion
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".