Managing Tensions Between Evaluation and Research
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.
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 | 0.730 | 0.698 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.015 | 0.101 |
| Scholarly communication | 0.049 | 0.051 |
| Open science | 0.007 | 0.042 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".