Bibliographic record
Abstract
This paper explores the politics of resilience and recovery narratives by bringing critical ethnography and auto-ethnographic methods to bear on my own experiences with storytelling distress in different contexts. Inviting people with lived experience to share their stories is now common practice in education, mental health, and broader community venues. Yet even when the intent of the stories shared are to offer systemic critique of mental health epistemes, it is difficult to hear such stories beyond the psychiatric gaze. I argue that individual storytelling practices now get processed through resiliency and recovery metanarratives that continue to position both the problem and its potential solution at the level of individual bodies. By offering an account of my own experiences of storytelling, I explore the limits, risks, and productive functions of this practice. This includes how such narratives, in accumulation, can reify conceptions of the resilient and recovered subject and thus help solidify mental health truth regimes.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".