Studies Are Not Enough: The Necessary Transformation of Evaluation
Bibliographic record
Abstract
Abstract: The authors contend that developments in the public and not-for-profit sectors over the last decade or so have profound implications for the profession of evaluation, implications that are not being adequately addressed. They argue that evaluation needs to transform itself if it wishes to play a significant role in the management of organizations in these sectors. Beyond traditional evaluation studies, evaluators working in public and not-for-profit organizations need to (a) lead the development of results-based management systems, (b) using this and all available evaluative information, strengthen organizational learning and knowledge management, and (c) create analytic streams of evaluative knowledge. Failing to grasp these challenges will result in a marginalized and diminished role for evaluation in public and not-for-profit sector management.
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 | 0.354 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.077 |
| Scholarly communication | 0.029 | 0.044 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.011 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".