Using evaluation theory in priority setting and resource allocation
Bibliographic record
Abstract
PURPOSE: Public sector interest in methods for priority setting and program or policy evaluation has grown considerably over the last several decades, given increased expectations for accountable and efficient use of resources and emphasis on evidence-based decision making as a component of good management practice. While there has been some occasional effort to conduct evaluation of priority setting projects, the literatures around priority setting and evaluation have largely evolved separately. In this paper, the aim is to bring them together. DESIGN/METHODOLOGY/APPROACH: The contention is that evaluation theory is a means by which evaluators reflect upon what it is they are doing when they do evaluation work. Theories help to organize thinking, sort out relevant from irrelevant information, provide transparent grounds for particular implementation choices, and can help resolve problematic issues which may arise in the conduct of an evaluation project. FINDINGS: A detailed review of three major branches of evaluation theory--methods, utilization, and valuing--identifies how such theories can guide the development of efforts to evaluate priority setting and resource allocation initiatives. Evaluation theories differ in terms of their guiding question, anticipated setting or context, evaluation foci, perspective from which benefits are calculated, and typical methods endorsed. ORIGINALITY/VALUE: Choosing a particular theoretical approach will structure the way in which any priority setting process is evaluated. The paper suggests that explicitly considering evaluation theory makes key aspects of the evaluation process more visible to all stakeholders, and can assist in the design of effective evaluation of priority setting processes; this should iteratively serve to improve the understanding of priority setting practices themselves.
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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 | 0.198 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.019 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".