The “What” and “Why” of (Un)Ethical Evaluation Practice: A Meta-Narrative Review and Ethical Awareness Framework
Bibliographic record
Abstract
There is growing recognition of the complex moral and ethical tensions associated with evaluation practice. However, there are scant evidence-informed frameworks for cultivating ethical awareness or informing ethical deliberation across the evaluation landscape. Thus, we aimed to synthesize research evidence on evaluation ethics, and draw on these findings to develop an evidence-informed evaluation ethics framework. Our methodological approach involved, first, conducting a meta-narrative review of empirical studies on evaluation ethics. Specifically, we conducted a systematic peer-reviewed and grey literature search, then identified, extracted, and thematically organize data from 20 studies that meet inclusion criteria. Second, in consultation with an ethicist, we curated findings on ethical concerns within an integrated evaluation ethics framework. Our results illustrate six thematic patterns of research inquiry on evaluation ethics and highlight trends, and gaps. The ethics framework (ACAP) we develop includes four multi-faceted categories. It outlines six Accountabilities (where ethical consideration is owed), illustrates how ethical Concerns can manifest in practice, and outlines diverse stakeholder groups’ Agency over the management of ethical concerns. Critically, it outlines five meta-categories of ethical principles (P) including systematic and transparent inquiry, accordant self-determination, fairness, beneficence and non-maleficence, and reflexive stewardship. Implications for priming ethical awareness, navigating ethical conflicts, and advancing evaluation ethics education and research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.343 | 0.506 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.025 | 0.015 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".