Bridging Research‐Practice Tensions: Exploring Day‐to‐Day Engaged Scholarship Investigating Sustainable Development Challenges
Bibliographic record
Abstract
This paper adds to literature on engaged scholarship by exploring how previous experience, study expectations, and multiple identities are key factors that shape how management researchers perceive and experience the research‐practice divide in sustainable development research. Highlighting ways to navigate tensions in engaged scholarship, the authors identify five major strategies: remembering the purpose of the research, emphasizing relationships, engaging in self‐learning, practicing reflexivity, and framing emerging results. To do so, the authors draw on findings from three management research projects which sought to use engaged scholarship to address the sustainable development challenges of homelessness, Indigenous approaches to economics and development, and sustainability reporting in higher education. Taking a collaborative auto‐ethnographic approach to analyzing their experiences as researchers, the authors demonstrate the potential for future management researchers to utilize a similar methodology to improve engaged scholarship research focused on sustainable development challenges.
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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 | 0.051 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.035 |
| Scholarly communication | 0.026 | 0.026 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".