What Meaning Can We Make Together? On Learning to Code Qualitative Research Data with Graduate Students in Education
Bibliographic record
Abstract
Graduate students in education are often expected to collect and code data, but the practice of how to do this with qualitative data is not often made visible. In October 2022, we gathered as a collective of graduate students and a professor to address this gap. We asked, what might it look like to learn about coding together? What informs the decisions we make during the coding process? Drawing on a workshop where we coded data from Dr. Burkholder’s research with 2SLGBTQI+ youth, we discussed how we approached the data from multiple lenses and perspectives. In this inquiry, we have traced the growth of our collective understanding of coding practices and show how we made sense of the data as we engaged in dialogue with each other. This paper reveals the intersections of our learning and creates a space for our accumulated knowledge using collaborative modes of inquiry. We argue that there is pedagogical value for graduate students in education and for qualitative researchers in making coding practices explicit.
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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 | 0.486 | 0.499 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.036 | 0.139 |
| Scholarly communication | 0.043 | 0.061 |
| Open science | 0.011 | 0.057 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".