Strategies for Generating Deliberately Emergent Qualitative Research Designs
Bibliographic record
Abstract
In carrying out research, qualitative scholars routinely struggle with having to navigate between planned and emergent research design strategies. Pressure from funders and gatekeepers to plan research can be high, but too much planning can interfere with the ethos of discovery that characterizes inductive qualitative research. On the other hand, study designs that are overly emergent present their own array of risks. In this essay, I argue for the integration of planned and emergent approaches to qualitative research design. I outline strategies for making planned research designs more reflexive and emergent, and strategies for making emergent research designs more directive and planned. I present two competencies—conceptual nimbleness and methodological reflexivity—that can be helpful for designing studies in this way and discuss how these deliberately emergent designs should be reported, with a view to enhancing the transparency and trustworthiness of qualitative research methods more generally.
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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 | 0.460 | 0.522 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.018 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".