Mysteries and Qualitative Research? Review of Mats Alvesson and Dan Kärreman’s Qualitative Research and Theory Development: Mystery as Method
Bibliographic record
Abstract
In an era of postmodern and social constructionist thought, qualitative researchers have experienced method as a mess. This time of conflict and tension has contributed to concerns and questions about researchers’ interpretive and reflexive contributions to the study of social reality. Into these confusing times, Mats Alvesson and Dan Kärreman, social constructionist researchers, take a novel approach to how qualitative research can inform theory development. They suggest researchers embrace the mysteries when trying to make sense of social situations by taking a reflective and interpretive approach towards their empirical material to create results that can challenge established theory and thus inspire novel lines of theory development.
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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 | 0.158 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.004 | 0.015 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| 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 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".