Developing a Framework for Research Evaluation in Complex Contexts Such as Action 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
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.
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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 | 0.112 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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