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.
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 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 | 0.028 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".