Managing Evaluataion: Responding to Common Problems with a 10-Step Process
Bibliographic record
Abstract
Abstract: There is now a clear choice of frameworks for managing program evaluation—the managing of one or more studies or the managing of an evaluation capacity building structure and process. This is a distinction with a difference, and this article conceptualizes that difference and shows how the two frameworks understand three problems common to program evaluation: (a) lack of systematic integration within a larger program improvement process, (b) difficulty in finding an appropriate evaluator, and (c) lack of appropriate conceptualization prior to the inception of the evaluation study. Two practice-based approaches to these problems are presented and interpreted using the two frameworks. These frameworks show clear distinctions and differences between the two managerial approaches. These are practice-tested approaches developed over 30 years of doing and managing evaluations in an evaluation unit in the United States, where there are seemingly clear differences with Canada in at least the public sector and in practices around stakeholder participation in relation to use practices. Our experience shows that program managers and managers of program evaluation services have clear choices in how they manage program evaluation in the public and nonprofit sectors across public health and other human services, and these choices have implications for organizational development, managing an evaluation unit, and interorganizational relations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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 teacher head, 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".