Introduction to Professionalizing Evaluation: A Global Perspective on Evaluator Competencies
Bibliographic record
Abstract
To have competencies or not to have competencies? That now seems to be a question for program evaluators and evaluation associations from around the globe. After roughly 50 years, people in a variety of settings are debating whether or not the time has arrived for a formal statement of the unique—or at least distinctive—knowledge, skills, and attitudes required for practitioners of program evaluation. Although program evaluation is a growing practice that has become a recognized field of vocation and study, wide interpretations of what competencies are necessary to guide evaluation practice remain. Commentators have provided many arguments, both positive and negative, surrounding the development, implementation, and potential use of competencies. Some point to the positive potential of the field’s coming to agreement on a core set. By contrast, not everyone is enthusiastic about the potential that a declaration of competencies might hold. As a statement by the United Kingdom Evaluation Society (UKES) summarizes, Some fear that it might provide a stranglehold on what evaluators can do; that it could not cover the variety of competencies needed for different evaluations; and that it might provide commissioners and managers of an evaluation with an inflexible list of competencies that would hold evaluators to account in unhelpful ways. (UKES, 2002, n.p.)
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| grok | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| opus | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.084 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.082 |
| Scholarly communication | 0.021 | 0.030 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.019 | 0.026 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".