Performance measurement as precursor to organizational evaluation capacity building
Bibliographic record
Abstract
The widespread use of performance measurement and program evaluation in public administrations worldwide has been met with varying degrees of success, in terms of increased transparency and effectiveness. In many cases, the lack of impact of these functions is attributed to insufficient organizational knowledge and capacity to implement proper monitoring and evaluation systems. Both of these management tools are recognized in their own right as having the potential to contribute to ongoing decision-making and budgetary allocations within public organizations; however, they have complementary roles in terms of producing ongoing versus periodic information, and focusing on outputs and early outcomes versus longer-term program objectives. This paper attempts to bridge these two functions by proposing that performance measurement can act as a precursor to the development of organizational evaluation capacity, by providing some of the building blocks required to develop an evaluative culture within an organization. Five models of organizational evaluation capacity were analyzed to identify the extent to which performance measurement contributes to evaluation capacity building.
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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 | 0.072 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".