Evaluations that ‘leave no one behind’: Decolonizing Canada’s international assistance evaluations in Africa
Bibliographic record
Abstract
The role of evaluation in reaching development outcomes, such as the Sustainable Development Goals, is key. However, there are growing calls from African evaluators for the transformation and decolonization of evaluation to ensure that development ‘Leaves no one behind.’ Despite Canada’s focus on equitable development and partnerships through practices such as the Feminist International Assistance Policy, significant challenges remain. Through an examination of Africa, which is a primary focus of international assistance in Canada, this study investigates how decolonization in evaluation can be operationalized. Through a literature review, expert and informant interviews, and jurisdictional scan, four policy options are analyzed and recommended through an implementation framework. The short-term recommendations call for more meaningful engagement of African evaluation approaches through evaluation terms of references and evaluation steering committees, and the creation of knowledge sharing plans. The long-term recommendations call for the implementation of evaluation-capacity-building projects and a pre-qualified pool of evaluators and firms from Africa.
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.
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 | 0.054 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.032 | 0.020 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".