“The Best Teacher Is Also a Student”: Improving Qualitative Research Literacy by Learning From My Mistakes
Bibliographic record
Abstract
This article is based on the 2022 Earle F. Zeigler Lecture Award that I presented in Atlanta, Georgia. For this paper, I reflect upon my career as a qualitative sport management researcher, with a specific focus on the mistakes I have made. I have two objectives with this paper. One objective is to advocate for continued learning about and rethinking how we conduct qualitative research. The second objective is to highlight ways in which we, as a field, can improve our qualitative research literacy. In the paper, I discuss eight learnings on the topics of ontologies and epistemologies, research designs, themes, pseudonyms, rigor, generalizability, positionality, and the publisher SAGE. In learning from my mistakes, we can be better consumers, producers, and evaluators of qualitative research.
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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 | 0.418 | 0.513 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.035 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".