In Plain Sight or Just Plain Obscured?: A Review of Professional Evaluation Associations’ Frameworks for Evaluation Practice Supporting Equity, Diversity and Inclusion (EDI)
Bibliographic record
Abstract
With an increasing focus on integrating equity, diversity and inclusion (EDI) in evaluation practice and products, there is an accompanying need to examine what structural supports exist that are provided by professional evaluation associations. This contribution systematically examines the competencies of five professional evaluation associations from Africa, Australia, Europe and North America to identify how evaluators can align integrating EDI in thei evaluation practice to professional competency domains. Also offered is a summary of thoughts on training opportunities for integrating EDI given review findings. Professional evaluation association websites were reviewed during May through to July of 2022. Content was downloaded into an Excel spreadsheet, and organized for EDI review purposes by competency domains, subdomains which occurred during August and September of 2022. The presentation of EDI content in evaluator competencies was found to be highly varied; variations were found in tone/theme, principles, and domains and subdomains.
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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 | 0.068 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".