When Narrative is Impossible: Difficult Knowledge, Storytelling, and Ethical Practice in Narrative Research and Pedagogy in Music Education
Bibliographic record
Abstract
Stories impel us to grapple with the humanity of another. Using story to recount experience, however, raises both challenges and questions. This paper explores the complexities that arise when narrative researchers attempt to render stories of trauma. I draw upon what Deborah Britzman (1998) calls “difficult knowledge” to explore what encounters with stories of trauma may produce, and I consider both the potential of narrative research and the pedagogical potential of both stories and music to facilitate wrestling with difficult knowledge. I grapple with two related questions: 1) What considerations should be taken into account to engage ethically in narrative research, particularly narratives that emanate from trauma or that include stories of trauma? and 2) What considerations should be taken into account when sharing stories of trauma as an educator? I then consider both the impossibility of representation within narrative in light of difficult knowledge, and further examine how Delbo’s (1995/2014) “useless knowledge” unsettles straightforward understandings of difficult knowledge in pedagogy and in research. Finally, I explore implications for researchers and educators, followed by an examination of a politics of refusal in telling, representing, or engaging with story.
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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 | 0.071 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.165 |
| Scholarly communication | 0.033 | 0.040 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".