Reflexive Accounts and Accounts of Reflexivity in Qualitative Data Analysis
Bibliographic record
Abstract
While the importance of being reflexive is acknowledged within social science research, the difficulties, practicalities and methods of doing it are rarely addressed. Thus, the implications of current theoretical and philosophical discussions about reflexivity, epistemology and the construction of knowledge for empirical socio-logical research practice, specifically the analysis of qualitative data, remain under-developed. Drawing on our doctoral experiences, we reflect on the possibilities and limits of reflexivity during the interpretive stages of research. We explore how reflexivity can be operationalized and discuss reflexivity in terms of the personal, interpersonal, institutional, pragmatic, emotional, theoretical, epistemological and ontological influences on our research and data analysis processes. We argue that data analysis methods are not just neutral techniques. They reflect, and are imbued with, theoretical, epistemological and ontological assumptions – including conceptions of subjects and subjectivities, and understandings of how knowledge is constructed and produced. In suggesting how epistemological and ontological positionings can be translated into research practice, our chapter contributes to current debates aiming to bridge the gap between abstract epistemological discussions and the nitty-gritty of research practice.
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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 | 0.353 | 0.422 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.011 | 0.085 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".