Qualitative researchers as modern day Sophists? Reflections on the qualitative–quantitative divide
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.180 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it