Bibliographic record
Abstract
The “narrative turn” in healthcare urges that listening to patient stories is morally and medically important. I largely agree. However, not everyone has equal access to telling stories and to having them heard, and so not everyone equally benefits from a narrative turn. Importantly, injustice and oppression often manifest in silencings, and so uncritically focusing on available stories risks upholding existing disparities. My dissertation argues that we should be concerned not only with how to engage with patient stories, but also to more critically engage with silences. First, I argue that narrative approaches require a more critical account of silence and silencing than current literature admits. I propose an operating account of silences as disruptions in expected or valued modes of communication, and which considers patient silences neither as mere absences nor restricted to speech. Second, I argue that different varieties of silencing demand different responses, and that these cannot be uniformly reduced to the usual calls to share one’s story, to break the silences, or to become more virtuous listeners. Third, I elaborate on these arguments by examining varieties of silencing that are often neglected in existing literature: silencing as a strategy of resistance, silencing in suicide prevention research, and silencings maintained by built environments. Ultimately, I argue that narrative approaches to healthcare must critically attend to the variety and complexity of patient silences. By better understanding the complexities of silences and silencings, we are better positioned to identify and respond to silences as they appear. Where existing literature argues for developing competencies in listening and engaging with stories, my arguments call for more critical attention to the preconditions of stories and silences, to the presumed value of stories over silence, and to the potentials of choosing silence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.085 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".