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Record W3193643485 · doi:10.1108/sej-10-2020-0099

Evaluating and improving the contributions of university research to social innovation

2021· article· en· W3193643485 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSocial enterprise journal · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsRoyal Roads University
FundersSocial Sciences and Humanities Research Council of CanadaCanada Research ChairsRoyal Roads University
KeywordsGovernment (linguistics)SociologyQuality (philosophy)Qualitative researchKnowledge managementPublic relationsEngineering ethicsManagement sciencePolitical scienceEngineeringComputer scienceSocial science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.238
GPT teacher head0.546
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it