Finding Theory–Method Fit: A Comparison of Three Qualitative Approaches to Theory Building
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 article, together with a companion video, provides a synthesized summary of a Showcase Symposium held at the 2016 Academy of Management Annual Meeting in which prominent scholars—Denny Gioia, Kathy Eisenhardt, Ann Langley, and Kevin Corley—discussed different approaches to theory building with qualitative research. Our goal for the symposium was to increase management scholars’ sensitivity to the importance of theory–method “fit” in qualitative research. We have integrated the panelists’ prepared remarks and interactive discussion into three sections: an introduction by each scholar, who articulates her or his own approach to qualitative research; their personal reflections on the similarities and differences between approaches to qualitative research; and answers to general questions posed by the audience during the symposium. We conclude by summarizing insights gleaned from the symposium about important distinctions among these three qualitative research approaches and their appropriate usages.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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