From the Field to the Field: Mapping a Landscape of Qualitative Research Through Scholars’ Personal Letters
Bibliographic record
Abstract
Despite the growth of qualitative research, we lack a systematic understanding of the lived experiences of qualitative scholars themselves. Our study is motivated by the intuition that by shedding light on the “map makers behind the maps” we may gain a novel view of the field: of the assumptions, emotions, fears and hopes that anchor extant qualitative theorizing. In this spirit, we solicited personal letters from a sample of North American and Western European management and entrepreneurship scholars, inviting them to reflect on their experiences as bases for articulating insights and advice for future researchers. Our letters revealed three distinct “maps of the field”: “roadmaps”; “political maps”; and “pictorial maps.” These maps stressed different features, distinct temporal orientations (past or future), emotions (positive, negative, or mixed), and “navigation advice.” Based on these various “maps” and insights, we draw theoretical and practical implications for future qualitative research.
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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 | 0.109 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".