Proposing the “MIRACLE” Narrative Framework for Providing Thick Description in Qualitative Research
Bibliographic record
Abstract
Thick description of qualitative findings is critical to improving the transferability of qualitative research findings as it allows researchers to assess their applicability to other contexts and settings. However, what thick description entails and how it should be carried out is often missing or insufficiently described. While expert qualitative researchers may be familiar with the concept, the wide variety of meanings and interpretations of thick description in the literature may make it difficult for novice qualitative researchers to understand this concept when reporting qualitative findings. The purpose of this paper is to propose the “MIRACLE” narrative framework for providing thick description in qualitative research. We developed this framework based on a critical review of theoretical literature about thick description and writing in qualitative research, as well as our personal experiences conducting, writing, and publishing qualitative studies. The proposed framework can be valuable for improving the reporting quality and transferability of qualitative research findings.
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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 | 0.348 | 0.325 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.015 | 0.056 |
| Scholarly communication | 0.018 | 0.033 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".