Evaluating and improving the contributions of university research to social innovation
Bibliographic record
Abstract
Purpose The purpose of this study is to assess the contributions of graduate research to social innovation and change for learning and improved transdisciplinary practice. Universities, as centers of teaching and research, face high demand from society to address urgent social and environmental challenges. Faculty and students are keen to use their research to contribute to social innovation and sustainable development. As part of the effort to increase societal impact, research approaches are evolving to be more problem-oriented, engaged and transdisciplinary. Therefore, new approaches to research evaluation are also needed to learn whether and how research contributes to social innovation, and those lessons need to be applied by universities to train and support students to do impactful research and foster an impact culture. Design/methodology/approach This paper uses a theory-based evaluation method to assess the contributions of three completed doctoral research projects. Each study documents the project’s theory of change (ToC) and uses qualitative data (document review, surveys and interviews) to test the ToC. This paper uses a transdisciplinary research (TDR) quality assessment framework (QAF) to analyze each projects’ design and implementation. This paper then draws lessons from the individual case studies and a comparative analysis of the three cases on, namely, effective research design and implementation for social transformation; and training and support for impactful research. Findings Each project aimed to influence government policy, organizational practice, other research and/or the students’ own professional development. All contributed to many of their intended outcomes, but with varying levels of accomplishment. Projects that were more transdisciplinary had more pronounced outcomes. Process contributions (e.g. capacity-building, relationship-building and empowerment) were as or more important than knowledge contributions. The key recommendations are for: researchers to design intentional research, with an explicit ToC; higher education institutions (HEI) to provide training and support for TDR theory and practice; and HEIs to give more attention to research evaluation. Originality/value This is the first application of both the outcome evaluation method and the TDR QAF to graduate student research projects, and one of very few such analyses of research projects. It offers a broader framework for conceptualizing and evaluating research contributions to social change processes. It is intended to stimulate new thinking about research aims, approaches and achievements.
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 | 0.555 | 0.675 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.023 | 0.014 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".