A comment to Gobo: the next challenge – from mixed to merged methods
Bibliographic record
Abstract
Purpose The purpose of this paper is to further the discussion on points made by Giampietro Gobo, provide additional information on the place of qualitative research in management, and question the space of merged methods. Design/methodology/approach Use a conversational approach as well as a review of qualitative vs quantitative research in three top tier journals for the years 2013-2016 (by a simple count). Findings Quantitative methods remain very much mainstream in management research, yet one finds that for one of the journals, space is evenly shared between qualitative and quantitative methods. Research limitations/implications This is a viewpoint and does not offer a systematic review of all top tier management journals. Originality/value It is hope that with this viewpoint debate as to the space of qualitative research, and merged methods can be stimulated.
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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 | 0.046 | 0.242 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.019 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.029 | 0.054 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".