What we can learn from the international program for development evaluation training (IPDET)
Bibliographic record
Abstract
Abstract The International Program for Development Evaluation Training, IPDET, ran in its first chapter from 2001–2016 in Ottawa, Canada. In 2018, it began its second chapter in Bern, Switzerland and continues today – an almost unheard‐of longevity for a summer short‐term training program. Over its first 16 years, IPDET trained more than 4000 persons in evaluation from more than 80 countries. During the time we report on in this chapter, IPDET consisted of a mix and match basic 2‐week core program in development evaluation and two subsequent weeks of 2‐ and 3‐day workshops for more in‐depth specialized evaluation training. Workshop topics were updated annually to remain current but included, for example, Cost‐Benefit Analytic Tools for Development Evaluation, Logic Models in Evaluation, Sampling Techniques I and II, Monitoring and Evaluating Governance in Africa, and Assessing the Outcomes and Impacts of Complex Programs. IPDET graduates have made many contributions to the field, such as establishing national evaluation associations, establishing and leading monitoring and evaluation units, producing country evaluation plans and national evaluation policies, and advancing evaluation in non‐profits, foundations, and the private sector. This reflective chapter examines IPDET's successes by identifying good practices for short‐term evaluation training programs. We review nine major factors contributing to IPDET's longevity in increasing the availability and diversity of evaluators worldwide and examine research on good training practices for short‐term adult evaluation training. Based on IPDET's experience, we suggest additional good practices for evaluation training programs.
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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 | 0.141 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".