Qualitative researchers as modern day Sophists? Reflections on the qualitative–quantitative divide
Bibliographic record
Abstract
This paper presents some of the questions or difficulties quantitative researchers might have when reading or thinking about qualitative methods. These issues include whether qualitative data differ from anecdotes, the idea that qualitative research is nonexperimental and is purely descriptive, and the ‘borrowing’ of quantitative concepts and giving them qualitative names. These questions were explored through discussion with three qualitative researchers. All the researchers emphasised that an important function of qualitative research is to provide context. The issues are discussed and contrasted with similar difficulties with quantitative methods. The idea that quantitative researchers are interested in measuring psychological phenomena, whereas qualitative researchers are interested in the interpretation of phenomena is explored. It is concluded that bringing quantitative and qualitative researchers together as collaborators would allow for richer data and, perhaps, bring us closer to the ‘truth’.
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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 | 0.373 | 0.300 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.020 | 0.110 |
| Scholarly communication | 0.029 | 0.047 |
| Open science | 0.007 | 0.021 |
| Research integrity | 0.014 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".