Witnessing the extraordinary : investigating the accomplishments of the ALGC program
Bibliographic record
Abstract
This study investigates the reasons behind the achievements of the Adult Learning and Global Change (ALGC) program, an international online master’s program developed and managed by four universities in Canada, Sweden, South Africa and Australia: The University of British Columbia (Canada), Linköping University (Sweden), The University of the Western Cape (South Africa) and three different universities in Australia (where the original partner, University of Technology Sydney, was replaced by Monash University, which is now being replaced by Australian Catholic University). The twelve individuals who have had leadership roles in the program since it began in 2001 were interviewed, and their answers to the same open ended questions provided the data for analysis. Based on their responses, it was possible to identify the six stages in the development of the program, the many accomplishments of the program from a variety of viewpoints (historical, educational, collaborative, administrative and personal), the different threats and weaknesses that endangered the program (and the way they were addressed), and, finally, the explanations for the accomplishments of the program. The conclusion is that thanks to its competent and committed leaders, a creative and innovative program, and constructive and caring relationships, the ALGC program has not only survived but thrived.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".