Teaching Africa-Rooted Evaluation: Using a “Model Client” Innovation to Help Shift the Locus of Knowledge Production
Bibliographic record
Abstract
Abstract: Evaluators working on the African continent are increasingly tasked with reflecting critically on how they might integrate African methods, culture, and knowledge systems into both evaluation teaching and practice. This practice note reflects on one small but potentially significant step toward this: revisiting how we deliver our Principles of Programme Evaluation module at the University of Cape Town. Our idea, which we call a “model client” approach, was to bring on board the evaluation client as a co-learner in the classroom environment. Through a series of instructor-facilitated client-student engagements, which unfolded within the classroom space, we (the instructors, students, and client) arrived at a co-created understanding of the program logic and co-determined the evaluation questions and evaluation approach. Key challenges in implementing this approach included managing the client’s sense of vulnerability, student inexperience in evaluation theory and practice, and a conspicuous shortage of African-generated evaluation case studies and texts.
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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 | 0.064 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".