Developing a Framework for Research Evaluation in Complex Contexts Such as Action Research
Bibliographic record
Abstract
Early investigation led the Evaluative Study of Action Research (ESAR) team to conclude that the complexity of a global, large scale study (evaluation of more than 100 highly diverse action research [AR] projects) called for an overarching research evaluation framework that differed from traditional frameworks. This article details the flexible, rigorous, Evaluative Action Research (EvAR) framework developed to meet the complex demands of the diverse AR projects and the intent to conduct high engagement research evaluation. The EvAR fulfilled multiple overarching needs to: authentically collaborate, engage, and enhance ownership from the ESAR team and the AR project participants and boundary partners evaluated; be informed in decision making via strong reference support; be responsive and flexible yet meet accountability demands to track, demonstrate, and measure process, outcomes, and impacts of projects; use mixed-method data collection to enhance rigor of findings; and utilize a highly reflective and reflexive approach to the evaluation. Many of the latter needs align with underpinning principles and values in AR itself; that is, it is collaborative, consultative, democratic, reflective, reflexive, dialogical, and improvement oriented. Rationale for the framework is provided alongside full details of phases and implementation elements using the ESAR as an example. Throughout the article, features are highlighted that distinguish this new EvAR framework from others. The advantages of adopting a flexible framework, which aims to enhance engagement of those evaluated, are highly relevant to contexts beyond AR if ownership of evaluation outcomes is a goal.
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 | 0.601 | 0.364 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.013 | 0.095 |
| Scholarly communication | 0.041 | 0.036 |
| Open science | 0.011 | 0.024 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".