Six ways to get a grip on developing reflexivity statements
Bibliographic record
Abstract
Qualitative researchers have underscored the value and importance of being reflexive in the research process, yet existing guidelines or checklists on how to practically address reflexivity are often scant and scattered across studies. In this scholarly perspective, we review, analyse, and present an overview of conceptions of reflexivity. Further, we offer practical guidelines for addressing and developing reflexivity statements in qualitative research. We describe reflexivity as both a concept and a deliberate ongoing process that requires a certain level of researcher consciousness, reflection, introspection, self-awareness, and an analytic attention to the researcher's role in the research process at all stages. We highlight the notion that reflexivity offers researchers an opportunity to examine potential assumptions, through the continuous process of questioning, examining, accepting, and articulating our attitudes, assumptions, perspectives, and roles. We present six recommendations to promote dialogue on the practice of reflexivity among researchers from various ontological and epistemological communities and encourage them to develop their own reflexivity practices.
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How this classification was reachedexpand
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 | 0.020 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads 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".