A quality assessment framework for transdisciplinary research design, monitoring, and evaluation: Guidance for application
Bibliographic record
Abstract
Appropriate definitions and measures of quality are needed to guide research design and evaluation. Traditional disciplinary research approaches have well-established evaluation criteria and processes in which research quality is often narrowly defined. In contrast, emerging change-oriented transdisciplinary research (TDR) approaches integrate disciplines and include societal actors in the research process in multiple ways and contexts. Standard research assessment criteria are simply inadequate for TDR, and inappropriate use of standard criteria may disadvantage TDR proposals and impede the development of TDR. This paper presents a Quality Assessment Framework (QAF) designed for TDR, along with guidance for its application. The background outlines the origin of the framework in a systematic review of literature on the definition and assessment of transdisciplinary research quality, and the subsequent application, testing and refinement of the framework, including discussion of key revisions. It also compares the QAF with two other similar evaluation frameworks. The QAF is designed for a range of users, including: research funders and research managers assessing proposals; researchers designing, planning, and monitoring a research project; and research evaluators assessing projects ex-post . The Framework is organized as: • Four principles of TDR quality • Specific criteria aligned with each principle • Standardized four-point scoring
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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 | 0.681 | 0.604 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.026 | 0.024 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.020 | 0.020 |
| Open science | 0.012 | 0.017 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".