Exploring Opportunities & Challenges in Qualitative Meta-Studies
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
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.
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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