The Changing Landscape of Development Evaluation Training: A Rapid Review
Bibliographic record
Abstract
The World Bank Independent Evaluation Group (IEG) works to improve development results through excellence in evaluation. A key part of this mandate focuses on developing the Bank’s client countries’ capacities in monitoring and evaluation. To this end, IEG developed the International Program for Development Evaluation Training (IPDET) in 2001, and this executive training program has been implemented since then in partnership with Carleton University in Ottawa, Canada. IPDET is managed by Carleton University but has received substantial in-kind (through technical experts) and financial support over the years from IEG. IPDET was conceived to offer a one-of-a-kind learning program for filling a gap in development evaluation training. However, there is broad recognition that the landscape is changing, with increasing numbers of organizations providing monitoring and evaluation (ME) training in some form, an evolving mix of formal graduate degree and certificate programs preparing evaluators, innovations in learning supported by new technologies, and the growing engagement of local networks and evaluation associations in evaluation capacity development. In this context, IEG has commissioned a rapid review of the current landscape for ME training to develop an understanding of the current context in which IPDET operates. Finally, training programs focused specifically on development evaluation were of particular interest for this review. The distinction between ‘evaluation’ and ‘development evaluation’ is arguably an important one, with increasing attention focused on what kinds of peer groups and curriculum are needed to effectively build the ME skills and knowledge relevant for a developing country context.
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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 | 0.139 | 0.218 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".