Defining and assessing research quality in a transdisciplinary context
Bibliographic record
Abstract
Research increasingly seeks both to generate knowledge and to contribute to real-world solutions, with strong emphasis on context and social engagement. As boundaries between disciplines are crossed, and as research engages more with stakeholders in complex systems, traditional academic definitions and criteria of research quality are no longer sufficient—there is a need for a parallel evolution of principles and criteria to define and evaluate research quality in a transdisciplinary research (TDR) context. We conducted a systematic review to help answer the question: What are appropriate principles and criteria for defining and assessing TDR quality? Articles were selected and reviewed seeking: arguments for or against expanding definitions of research quality, purposes for research quality evaluation, proposed principles of research quality, proposed criteria for research quality assessment, proposed indicators and measures of research quality, and proposed processes for evaluating TDR. We used the information from the review and our own experience in two research organizations that employ TDR approaches to develop a prototype TDR quality assessment framework, organized as an evaluation rubric. We provide an overview of the relevant literature and summarize the main aspects of TDR quality identified there. Four main principles emerge: relevance, including social significance and applicability; credibility, including criteria of integration and reflexivity, added to traditional criteria of scientific rigor; legitimacy, including criteria of inclusion and fair representation of stakeholder interests, and; effectiveness, with criteria that assess actual or potential contributions to problem solving and social change.
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 | 0.756 | 0.823 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.043 | 0.027 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".