Multidisciplinary graduate training in social research methodology and computer-assisted qualitative data analysis: a hands-on/hands-off course design
Bibliographic record
Abstract
Drawing on the experience of training graduate students and researchers in qualitative and mixed-methods analysis since the mid-1990s, the authors reflect on the evolution of a multidisciplinary graduate course developed in a Canadian university since 2007. The hands-on/hands-off course design based on the use of NVivo was developed in parallel with a conceptual and reflexive position on methodology and multidisciplinarity in the context of an action research supported by their institution. This article exposes the four basic components of the course and the constructivist assessment model based on a term-long team analysis of original data created specifically for this purpose. The balance between collective and individual work, in-class and online exchanges, hands-on experience with the software and hands-off reflection is described and compared to recent pedagogical literature on the subject. The interest of this blended approach in fostering the development of the methodological expertise expected from researchers and graduate students, as well as the main issues and challenges facing this type of training in a research environment characterised by new approaches and tools, are discussed.
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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 | 0.074 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".