Honing the Craft of Qualitative Data Collection in Extreme Contexts
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
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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