Explicating Positionality: A Journey of Dialogical and Reflexive Storytelling
Bibliographic record
Abstract
Qualitative researchers must be aware of and explicit about their social background as well as political and ideological assumptions. To facilitate this awareness, we believe that researchers need to begin with their own story as they seek to understand the stories of others. Taking into account the vulnerable act of storytelling, it is salient to consider how to share personal narratives in an authentic way within academic settings. In this article, we share our process and reflections of engaging in reflexive and dialogical storytelling. The focus of the article is the re-storying of one researcher's experience as she and her research team explore her emotions and positionality prior to conducting research on First Nations men's narratives of identity. We integrate a series of methodological lessons concerning reflexivity throughout the re-storying.
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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 | 0.199 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.024 | 0.095 |
| Scholarly communication | 0.036 | 0.039 |
| Open science | 0.008 | 0.026 |
| Research integrity | 0.007 | 0.017 |
| 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".