A Reflexive Lens: Exploring Dilemmas of Qualitative Methodology Through the Concept of Reflexivity
Bibliographic record
Abstract
Reflexivity has emerged as a central and critical concept in the methodology of qualitative social research. However, the concept of reflexivity is defined and taken up in a wide variety of ways, and the assumptions underlying each incarnation have particular implications for research outcomes. This paper argues that these various conceptualizations of reflexivity offer the qualitative researcher a critical lens through which to analyze key methodological dilemmas. Exploring the concept of reflexivity in its various forms provides an entry-point for understanding key dilemmas in the epistemology of qualitative methodology, qualitative research relationships, and the evaluation of qualitative research. These dilemmas present important challenges in terms of the complex issues of power, knowledge production and subjectivity. This paper thus demonstrates that a reflexive exploration of methodological dilemmas can provide a starting point for assessing the consequences and transformative potential of our qualitative research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.522 | 0.458 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.014 | 0.159 |
| Scholarly communication | 0.037 | 0.040 |
| Open science | 0.008 | 0.027 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".