Developing the craft: reflexive accounts of doing reflexive thematic analysis
Bibliographic record
Abstract
:Thematic analysis (TA) is unique in that it does not come with a predetermined theoretical framework, leaving the researcher accountable to articulate methodological decisions made. As a community of qualitative scholars, we need to clearly articulate and define the theoretical foundations, assumptions, and parameters that guide our work and analysis. We also need to be transparent about our reflections during data analysis, sharing our tensions, struggles, and realizations. While the flexibility of TA can lead to poorly constructed and executed analysis, it also offers the ability to develop rich, detailed, and nuanced analysis. TA is not your ’simple go lucky‘ approach, rather the complexities, interaction, and creativity that reflexive TA offers is remarkable. While TA is one of the most commonly used methods to analyze qualitative data, there is considerable variability in how the method is understood and conducted. As a growing qualitative researcher, [Author A] was frustrated by the limited examples of the reflexive process of doing TA, and the lack of transparency of how the data analysis was carried out. She grappled with figuring outhowto conduct a high-quality TA. As an experienced qualitative researcher and a mentor to graduate students, [Author B] struggled to find ways to support and guide [Author A] to develop her craft. The experience brought her to reflect on her own use of TA and how her practice has evolved. In this manuscript, we use visual and written examples to show the active decisions made during analysis, struggles and rebounds, and how these aided us in understanding the process of reflexive TA.
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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 | 0.181 | 0.228 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.017 | 0.088 |
| Scholarly communication | 0.032 | 0.033 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".