Developing the craft: reflexive accounts of doing reflexive thematic analysis
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
: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.
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 | 0.099 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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