Honing the Craft of Qualitative Data Collection in Extreme Contexts
Bibliographic record
Abstract
Over the past several years, there has been ongoing dialog within our academic journals and the profession regarding the value of examining extreme, unconventional, or unsettling contexts in management research. These conversations have highlighted that perhaps more than ever, we as a society are facing unprecedented grand and perplexing challenges, and conducting research in unconventional or extreme settings can reveal complex dynamics or relationships that we may not understand otherwise. Less discussed, however, are methodological considerations for conducting research in unique contexts. As such, we aim to extend the explicit discussion of effective strategies for scholars who consider the perspectives and workplace realities of unusual or unconventional populations. We bring together a collection of reflective essays rooted in the authors’ experiences of collecting data from extreme contexts or unusual samples. We highlight how these rich experiences in the field required the authors to modify or extend methodological conventions with the goal of guiding scholars pursuing research in similarly unconventional contexts.
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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 | 0.617 | 0.690 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.020 | 0.069 |
| Scholarly communication | 0.031 | 0.031 |
| Open science | 0.011 | 0.031 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".