Exploring Opportunities & Challenges in Qualitative Meta-Studies
Bibliographic record
Abstract
In this panel symposium, we will discuss how management scholars can benefit from the ever-expanding body of qualitative evidence available in our field. Despite the growing interest in this area, there have been limited opportunities for dialogue on various issues to qualitative meta-studies and for exchanging insights across different divisions. We intend to bring together experts on qualitative knowledge syntheses, qualitative meta-studies, and qualitative research more generally. These experts will offer insights into both the most promising and contentious issues around qualitative knowledge synthesis in general, and more specifically, qualitative meta-studies. They will provide their perspective on several unresolved issues critical for advancing different types of qualitative meta-studies, including (1) onto-epistemological considerations in synthesizing qualitative evidence, (2) theory-building from qualitative meta-studies, and (3) quality criteria for evaluating qualitative meta-studies.
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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 | 0.868 | 0.909 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.030 | 0.032 |
| Science and technology studies | 0.013 | 0.048 |
| Scholarly communication | 0.040 | 0.048 |
| Open science | 0.016 | 0.036 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 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".