Decolonising national evaluation systems
Bibliographic record
Abstract
Background: The world is facing rapidly declining health of the climate and ecosystems on which all species depend, with wealth accumulating in the hands of a few, a result of unsustainable economic systems. Evaluation has the potential for a significant role in learning from the past and helping to guide a regenerative future, but for this, the approach to evaluation and the systems that produce them must be transformative and take on more holistic approaches to society and the planet. Objectives: This study aims to explore how cases of African national evaluation systems (NESs) apply elements of a decolonised social-ecological model and how this could be strengthened. Method: This study involves a constructive critical analysis of the South African and Benin NESs, drawing on literature on decolonising evaluation and a new institutionalism lens to the formation of post-colonial bureaucracies, tested in a webinar conversation around decolonising evaluation in November 2023. Results: The African NESs have embedded learning, exhibit both machine-based and ecological-based elements, and experience some decolonised aspects. A key limitation is the lack of involvement of communities in the systems. Conclusion: This study argues for: (1) allowing NESs to break from historical forms of bureaucratic functioning; (2) developing a systems-based approach as the basis for new thinking around NESs, strengthening their ecological aspects; (3) embracing the learning approaches we see in both countries; (4) embracing principles of participatory democracy and co-production by strengthening the voice of non-state actors, particularly citizens, in the formation of NESs and (5) changing power dynamics, in NESs and evaluations. Contribution: This article is contributing to a debate on how evaluation systems can be decolonised and power relations changed.
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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 | 0.083 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".