Mapping Evaluation Use: A Scoping Review of Extant Literature (2005–2022)
Bibliographic record
Abstract
Factors influencing evaluation use has been a primary concern for evaluators. However, little is known about the current conceptualizations of evaluation use including what counts as use, what efforts encourage use, and how to measure use. This article identifies enablers and constraints to evaluation use based on a scoping review of literature published since 2009 ( n = 47). A fulsome examination to map factors influencing evaluation use identified in extant literature informs further study and captures its evolution over time. Five factors were identified that influence evaluation use: (1) resources; (2) stakeholder characteristics; (3) evaluation characteristics; (4) social and political environment; and (5) evaluators characteristics. Also examined is a synthesis of practical and theoretical implications as well as implications for future research. Importantly, our work builds upon two previous and impactful scoping reviews to provide a contemporary assessment of the factors influencing evaluation use and inform consequential evaluator practice.
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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 | 0.073 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.057 | 0.056 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".