Bibliographic record
Abstract
Abstract House's classic Evaluating with Validity proposes three dimensions—truth, justice, and beauty—for evaluation validity. A challenge to achieving validity is balancing the priorities between these three dimensions when they conflict. This chapter examines the concept of validity and the values inherent in each of these dimensions and any choices between them. Our analysis of these inherent values and any prioritization between truth, justice, and beauty aims to help the evaluator confront the kinds of dilemmas faced when one's commitment to values, evaluation theories, or methodology comes up against conflicting realities for a particular evaluation. Striking an appropriate balance can be particularly challenging in contexts involving diverse cultures or even homogenous cultures of which the evaluator is not a part. We use two case examples to explore the issues in real‐life contexts.
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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 | 0.136 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.116 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".