Putting qualitative international business research in context(s)
Bibliographic record
Abstract
Abstract The Welch et al. (J Int Bus Stud 42(5):740–762, 2011) JIBS Decade Award-winning article highlights the importance of the contextualization of international business research that is based on qualitative research methods. In this commentary, we build on their foundation and develop further the role of contextualization, in terms of the international business phenomena under study, contemporaneous conversations about qualitative research methods, and the situatedness of individual papers within the broader research process. Our remarks are largely targeted to authors submitting international business papers based on qualitative research, and to the gatekeepers – editors and reviewers – assessing them, and we provide some guidance with respect to these three dimensions of context.
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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 | 0.152 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".