Bourdieu’s Reflexive Sociology as a Theoretical Basis for Mixed Methods Research
Bibliographic record
Abstract
Although mixing quantitative and qualitative methods is increasingly popular, there is insufficient theoretical rationale for doing so. Foremost among the legacy left to the social and behavioral sciences by French sociologist Pierre Bourdieu stands his emphasis on methodological reflexivity. Reflexive sociology as elaborated by Bourdieu is a self-referential methodology of social research, which turns methods of constructing the research object back on themselves so as to produce more accurate understanding of the social world. Using the sociology of alternative medicine as an illustration, this article casts Bourdieu’s reflexive sociology as a theoretical basis for mixed methods research that can offer insights into the interplay of structure and agency in human behavior.
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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 | 0.188 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.007 | 0.073 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".