Dissolving Dualisms: How Two Positivists Engaged with Non-Positivist Qualitative Methodology
Bibliographic record
Abstract
This is the story of how a chemical engineer and a medical microbiologist overcame their positivist training and deeply held disciplinary attitudes to engage with non-positivist qualitative methodology. Through a series of facilitated reflections they explored what helped and hindered their transition from positivist to non-positivist inquiry. To move forward they needed to acknowledge the extent and nature of the transition they were making, find metaphors to dissolve troubling dualisms, and balance a desire to reach out to others with the need to manage the very real sense of vulnerability that came with embracing subjectivity. Their experiences suggest that pragmatism may be a useful bridging framework for the growing number of academics from the science, technology, engineering, and math (STEM) disciplines turning to qualitative methodologists for help to move beyond positivist 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 | 0.221 | 0.275 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.032 | 0.096 |
| Scholarly communication | 0.042 | 0.032 |
| Open science | 0.009 | 0.044 |
| Research integrity | 0.014 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".