Rethinking the Challenge of Expressing Pain in Language: in literature, theory and medical practice
Bibliographic record
Abstract
Elaine Scarry’s The Body in Pain: the Making and Unmaking of the World (1985) proposed an analysis of pain and the concepts of language, imagination, subjectivity, social isolation. This thesis examines the link between language and pain in relation to Scarry’s assumption that it is extremely hard to accurately describe sentient pain in verbal and written forms of expression. Despite pain’s resistance to language, language holds the healing potential of softening pain. The process of “externalization” (the act of externalizing one’s pain into the material world outside the painful inner existence) is a starting point from which the treatment of pain can begin. However, in order to carry out the externalization, one has to express pain in language. I employ three case studies in order to determine whether Scarry’s assumption about pain’s resistance to language can be overcome: Leo Tolstoy’s novel The Death of Ivan Ilyich, Alphonse Daudet’s collection of personal notes In the Land of Pain and a scientific instrument– the McGill Pain Questionnaire. The thesis employs a multidisciplinary approach to pain in which cultural, social and biological aspects are taken into account. It also seeks to re-evaluate the single label of ‘pain’ and proposes to view pain as a multitude of experiences.
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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 | 0.035 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.013 | 0.116 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 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".