Using Evaluative Information Sensibly: The Enduring Contributions of John Mayne
Bibliographic record
Abstract
In this concluding article, we take stock of the diverse and stimulating contributions comprising this special issue. Using concept mapping, we identify eight evaluation themes and concepts central to John Mayne’s collective work: evaluation utilization, results-based management, organizational learning, accountability, evaluation culture, contribution analysis, theory-based evaluation, and causation. The overarching contribution story is that John’s work served to bridge the gaps between evaluation practice and theory; to promote cross-disciplinary synergies across program evaluation, performance auditing, and monitoring; and to translate central themes in evaluation into a cogent system for using evaluative information more sensibly. In so doing, John left a significant institutional and academic legacy in evaluation and in results-based management.
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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 | 0.052 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.016 |
| 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; 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".