Reflectivity and reflexivity in qualitative research and scholarship
Bibliographic record
Abstract
Abstract In this chapter, we first introduce four reflexive methodologies: autoethnography, duoethnography, narrative inquiry, and currere. Although these four approaches vary, they all involve inquirers telling stories about self and culture. Researchers working with these methodologies explore subjective yet socially situated, personal yet relational, and discursive yet emancipatory questions and topics. Describing these methodologies, we refer both to published accounts and literature and to specific chapters in this book. Central to these methodologies are the related concepts of reflection and reflexivity. Given their multiple levels, reflection and reflexivity assume a range of profiles, both individually and collectively. As a framework for “reading” reflection and reflexivity in this chapter, we consider the following structure: (a) what they are, (b) how they are enacted and mobilized within inquiry, and (c) what they yield within and from inquiry. Finally, we present an overview of the subsequent chapters in this volume.
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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 | 0.416 | 0.307 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.016 |
| 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 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".