Fold in the Cheese? An Approach to Teaching Qualitative Data Analysis to Students
Bibliographic record
Abstract
There are many elements of qualitative data analysis that may appear intangible to novice researchers. In this article, I present an approach to a data analysis workshop with students, where I do my best to avoid the instruction to “fold in the cheese,” as per the television series Schitt’s Creek. Students attend 90-minute workshops where they use an assortment of buttons to practice different strategies of qualitative analysis. The tactile mechanism of sorting objects has proven invaluable in workshops, as it helps students to physically organize their thoughts and takes pressure off to find the “right” answer. The nature of the items could also be adapted to meet students’ accessibility needs. The workshop has improved the quality of student writing in methods chapters and built students’ confidence as they approach their own data analysis processes, especially for master’s students and research assistants, where a full-length course may not be feasible.
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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 | 0.091 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".