Collaborative research and the scholarship of engagement: challenges for academic researchers
Bibliographic record
Abstract
The purpose of this qualitative, interpretive study was to explore the challenges associated with collaborative research and the scholarship of engagement particularly for academic researchers, to determine the value placed on collaborative research by both academics and community partners, and to investigate how community-university partnerships can be sustained. Academic researchers and community partners from a Community University Research Alliance (CURA) project in a Faculty of Education at a Canadian university, a CURA fellow graduate student, a representative from a knowledge mobilization unit at a university, and a Social Sciences and Humanities Research Council (SSHRC) official were included in the study population. Sixteen interviews in total were conducted and analyzed. The findings of this study suggest that collaborative research is challenging work. Administrative, relationship-related, cultural, and ethical challenges were highlighted. Administratively, it can be very difficult to manage a partnership with multiple projects, and its players having varying levels of understanding of collaborative research. One of the greatest administrative challenges is dealing with the changeover of players, and engaging participants who come to the project later. Clarity of expectations can be challenging since even when one attempts to develop protocols, it can be difficult to predict all issues that may arise. This study demonstrates that academics and community partners do not have a good understanding of each other’s cultural realities which can be the source of frustration when individuals do not understand why the other partner is behaving in a certain manner. Dealing with findings that may not be favourable to the community partner creates ethical challenges. This study demonstrates that the value of collaborative research depends on the perspective of the participant. Some academic researchers and community partners welcome the opportunity to work together in the co-creation of knowledge since they recognize that a more enriched product can be the end result. Other university researchers, holding steadfast to more traditional research, sometimes only engage in collaborative work to gain access to funding, and very quickly resort to more traditional methodologies. For some community partners, the research has little value to guide practice. Among factors highlighted for contributing to the potential success of collaborative partnerships, the level of participant buy-in is noted as having a definite effect. Minimizing the number of partnerships and allowing more than five years may be needed to grow sustainable partnerships. Looking for the “right fit” between partners where interests align could help. Knowledge mobilization units could be beneficial to help connect partners. The creation of memorandums of understanding, advisory committees, and a project manager position are highly recommended. Fostering strong leadership, incorporating succession planning, and the need for ongoing dialogue to help engage participants and create ownership are important. Time release supports for both academics and community partners, and university support through promotion and tenure practices that reward collaborative research involvement can help sustain community-university partnerships.
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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 | 0.266 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.043 | 0.114 |
| Scholarly communication | 0.040 | 0.041 |
| Open science | 0.008 | 0.034 |
| Research integrity | 0.010 | 0.017 |
| 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".