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Record W7033447376

Rethinking the Challenge of Expressing Pain in Language: in literature, theory and medical practice

2019· dissertation· en· W7033447376 on OpenAlexaboutno aff

Bibliographic record

VenueLeiden Repository (Leiden University) · 2019
Typedissertation
Languageen
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMultitudeRelation (database)Resistance (ecology)Pain and sufferingPoint (geometry)Order (exchange)Medical practiceMultidisciplinary approachPerspective (graphical)
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.207
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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