Critical Approach to Reflexivity in Grounded Theory
Bibliographic record
Abstract
A problem with the popular desire to legitimate one’s research through the inclusion of reflexivity is its increasingly uncritical adoption and practice, with most researchers failing to define their understandings, specific positions, and approaches. Considering the relative recentness with which reflexivity has been explicitly described in the context of grounded theory, guidance for incorporating it within this research approach is currently in the early stages. In this article, we illustrate a three-stage approach used in a grounded theory study of how parents of children with autism navigate intervention. Within this approach, different understandings of reflexivity are first explored and mapped, a methodologically consistent position that includes the aspects of reflexivity one will address is specified, and reflexivity-related observations are generated and ultimately reported. According to the position specified, we reflexively account for multiple researcher influences, including on methodological decisions, participant interactions and data collection, analysis, writing, and influence of the research on the researcher. We hope this illustrated approach may serve both as a potential model for how researchers can critically design and implement their own context-specific approach to reflexivity, and as a stimulus for further methodological discussion of how to incorporate reflexivity into grounded theory research.
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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 | 0.485 | 0.418 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.012 | 0.109 |
| Scholarly communication | 0.025 | 0.020 |
| Open science | 0.010 | 0.020 |
| Research integrity | 0.009 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 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".