22. Collaboratively reimagining teaching and learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While there are regular calls for African universities to improve their teaching, finding ways to do this within the resources available in already stretched institutions, and at the scale required, have proven elusive. This chapter is a reflexive exercise, discussing the work of an international partnership, Transforming Employability for Social Change in East Africa (TESCEA), that aimed to reshape habits of teaching and learning in four institutions of higher education. The authors explain how they sought to enable teaching for critical thinking and problem-solving, ensure degree programmes were relevant to social and economic needs by engaging employers and local communities, and learning environments enabled both young women and men to learn effectively. It offers reflections on the change observed, the ways in which this was achieved, and the challenges encountered. The authors hope it adds to understandings of how change can happen in higher education, particularly in resource-constrained settings.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it