Building an Evaluative Culture: The Key to Effective Evaluation and Results Management
Bibliographic record
Abstract
Abstract: As many reviews of results-based performance systems have noted, a weak evaluative culture in an organization undermines attempts at building an effective evaluation and/or results management regime. This article sets out what constitutes a strong evaluative culture where information on performance results is deliberately sought in order to learn how to better manage and deliver programs and services. Such an organization values empirical evidence on the results it is seeking to achieve. The article outlines and discusses practical actions that an organization can take to build and support an evaluative culture.
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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 | 0.408 | 0.372 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.022 | 0.060 |
| Scholarly communication | 0.050 | 0.026 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.008 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".