Another Way to Listen: Reflections on Listening in a Narrative Inquiry
Bibliographic record
Abstract
I encountered significant challenges in hearing Phoenix, a participant in a narrative inquiry into the health and well-being of women previously trafficked. My struggles to hear Phoenix lead me to constantly interrupt her. These interruptions were palpable and violent in their silencing. Yet, Phoenix continued to invite me to listen to her, calling me to better understand who I was and who I was becoming alongside her. In this paper, I reflect on ways to listen and the art of listening in narrative inquiry. I illustrate how maintaining a connection is vital for developing different ways of listening, a form of listening that is guided by ethical considerations and responsiveness. I show how listening serves as both a state of being and a mode of thinking within narrative inquiry.
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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 | 0.028 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.054 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.013 | 0.029 |
| 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".