Bibliographic record
Abstract
This article explores important questions around the often taken for granted approach to interviewing within narrative inquiry. When I applied an interview approach that emphasized the dialogical, performative, and social, tensions were provoked that muddied my assumptions and equilibrium. By sharing my story, I invite readers to reflect upon the researcher's role in interviewing. I address tensions that arose between (a) presence and performance, (b) equality and power, (c) leading and following, (d) insider and outsider, (e) influence and neutrality, and (f) trust and responsibility. I come to describe the craft of co-constructing stories with another as breathing in the mud—a dynamic process in which the researcher moves between the tensions of getting stuck in one moment and finding brilliant presence in the next. Discussion focuses on how a researcher might use tensions as catalysts that ignite clarity and advance how narrative interviewing is enacted.
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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 | 0.069 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".