Building Interdisciplinary Qualitative Research Networks: Reflections on Qualitative Research Group (QRG) at the University of Manitoba
Bibliographic record
Abstract
As qualitative research methodologies continue to evolve and develop, both students and experienced researchers are showing greater interest in learning about and developing new approaches. To meet this need, faculty at the University of Manitoba created the Qualitative Research Group (QRG), a community of practice that utilizes experiential learning in the context of social relationships to nurture social interaction, create opportunities to share knowledge, support knowledge creation, and build collaborations among all disciplines. While many other qualitative research networks such as the QRG may exist, little has been published on their early development or the activities that contribute to the growth and sustainability of active collaboration. To address this gap, the authors of the paper will share the steps taken in developing the QRG, including a needs assessment identifying members’ strengths and support needs, regular communication through a listserv, to the successful workshop based on the community of practice concept. Lessons learned during the initial development of the QRG are shared with the intent of contributing ideas for developing and supporting qualitative research in other institutions and prompting further consideration of ways to support and enrich every generation of qualitative researchers.
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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 | 0.203 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.047 | 0.032 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.012 | 0.019 |
| 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; 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".