Making Data From Drawing: How Step-by-Step Protocols Can Enrich Reflexive Inquiry in Qualitative Research
Bibliographic record
Abstract
In qualitative research, what happens when reflexivity is made explicit? In some instances, researchers may encounter phenomena that run counter to the cultural norms and expectations that shape their everyday lives. We present in this article a qualitative protocol titled A Method for Creative Reflexive Data in Autoethnography—an experimental method for creative reflexive data that enables researchers to follow a step-by-step protocol using drawing. This is achieved by presenting the analytical mechanics underlying each step of the experimental method, thereby making a researcher's reflexive process more visible in data production. We share two objectives in this article: First, to present a programmatic case for the protocol as a (more-than-reproducible) way of making any qualitative method more explicit; second, to report on the specifics of a new method that helps render the analytic process visible at each step.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.108 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".