Bibliographic record
Abstract
Engaging in self-narrative is often touted as a powerful antidote to the bad effects of illness. However, there are various examples of what may broadly be termed "aversion" to illness narrative. I group these into three kinds: aversion to certain types of illness narrative; aversion to illness narrative as a whole; and aversion to illness narrative as an essentially therapeutic endeavor. These aversions can throw into doubt the advantages claimed for the illness narrator, including the key benefits of repair to the damage illness does to identity and life-trajectory. Underlying these alleged benefits are two key presuppositions: that it is the whole of one's life that is narratively unified, and that one's identity is inextricably bound up with narrative. By letting go of these assumptions, illness narrative advocates can respond to the challenges of narrative aversions.
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 | 0.083 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.083 |
| Scholarly communication | 0.025 | 0.037 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.027 | 0.052 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".