The Use of Evaluability Assessments in Improving Future Evaluations: A Scoping Review of 10 Years of Literature (2008–2018)
Bibliographic record
Abstract
Since the beginning of the 21st century, evaluability assessments have experienced a resurgence of interest. However, little is known about how evaluability assessments have been used to improve future evaluations. In this article, we identify characteristics, challenges, and opportunities of evaluability assessments based on a scoping review of case studies published since 2008 ( n = 59). We find that evaluability assessments are increasingly used for program development and evaluation planning. Several challenges are identified: politics of evaluability; ambiguity between evaluability and evaluation, and limited considerations of gender equity and human rights. To ensure relevance, evaluability approaches must evolve in alignment with the fast-changing environment. Recommended efforts to revitalize evaluability assessment practice include the following: engaging stakeholders; clarifying what evaluability assessments entail; assessing program understandings, plausibility, and practicality; and considering cross-cutting themes. This review provides an evidence base of practical applications of evaluability assessments to support future evaluability studies and, by extension, future evaluations.
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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 | 0.171 | 0.352 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.036 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".