Managing Tensions Between Evaluation and Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Developmental evaluation (DE), essentially conceptualized by Patton over the past 30 years, is a promising evaluative approach intended to support social innovation and the deployment of complex interventions. Its use is often justified by the complex nature of the interventions being evaluated and the need to produce useful results in real time. Despite its potential advantages, DE appears not to have been very widely used in research. The authors of this article decided to use this emergent approach in two evaluative research projects in health promotion. This article, coming out of their experiences, aims to assess the appropriateness of DE in research and describes issues related to its use. First, DE is presented, along with the potential advantages of its use in research. This is followed by a discussion of tensions related its application encountered in two studies carried out by the authors. The key issues are related to the links between academic and evaluative objectives, the dual role of researcher and consultant, and the temporality of the process. Finally, weighing the advantages of DE against its challenges, the authors conclude with a diagnosis regarding the application of this approach in research.
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.
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 | 0.071 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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 it