The Program Evaluation Standards in Evaluation Scholarship and Practice
Bibliographic record
Abstract
Background: The Program Evaluation Standards that were developed and approved by the Joint Committee on Standards for Educational Evaluation have served as a resource to the broader evaluation field for over four decades. However, little evidence has been collected regarding the extent to which the standards have influenced the field through scholarship or professional practice. Purpose: This study seeks to estimate the prevalence of the Program Evaluation Standards in evaluation scholarship and professional practice. Setting: Not applicable. Intervention: Not applicable. Research Design: The study combines a systematic review of evaluation literature with a survey of American Evaluation Association (AEA) and Canadian Evaluation Society (CES) members. Data Collection and Analysis: A systematic review of articles published in 14 evaluation-specific journals from 2010 to 2020 was conducted to identify and typify articles citing the standards. Additionally, AEA and CES members were surveyed, with a focus on knowledge and use of the standards. Descriptive analyses are presented to quantify the prevalence of the standards in evaluation scholarship and practice, respectively. Findings: The systematic review revealed that 4.48% of the 4,460 articles published in 14 evaluation-specific journals from 2010 to 2020 contained some use of the standards. Survey results show that 53.14% of AEA members and 67.12% of CES members are familiar with the standards and that, among those with knowledge of the standards, most AEA (67.67%) and CES (71.74%) members use them at least “occasionally” in their professional work, education, and scholarship activities.
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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 | 0.435 | 0.584 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".