Cripping the pain scale: literary and biomedical narratives of pain assessment
Bibliographic record
Abstract
Establishing first a brief history of methods attempting to quantify pain before my close reading, I read both Biss' and Huber's accounts as performative explorations of the limitations of using linear pain scales for pain which is recursive and enduring. Considering both texts as cripistemologies of chronic pain, my literary analysis attends to their criticism of the pain scale, including its implicit reliance on imagination and memory, and how its unidimensionality and synchronic focus prove inadequate for lasting pain. For Biss, this surfaces as a quiet critique of numbers and a disturbance of their fixity, while Huber's criticism employs the motif of pain's legibility across multiple bodies to spell out alternative meanings of chronic pain.Crucially, this article proposes a crip and embodied approach for reading and responding to accounts of chronic pain's measurement, including Biss' and Huber's literary accounts, and the biomedical account of pains scales which this article reads alongside them. The article's analysis draws on my personal experience of chronic pain, neurodivergence and disability to demonstrate the generativity of an embodied approach to literary analysis. Rather than bowing to the impulse to impose false coherence on my reading of Biss and Huber, my article foregrounds the impact of the re-reading, misreading, cognitive dissonance and breaks necessitated by chronic pain and processing delays on this analysis. In bringing an ostensibly crip methodology to bear on readings of chronic pain, I hope to invigorate discussions on reading, writing and knowing chronic pain in the critical medical humanities.
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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.008 | 0.044 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".