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Where Paralytics Walk and the Blind See

2022· book· en· W4383041408 on OpenAlexaboutno aff
Mary Dunn

Bibliographic record

VenuePrinceton University Press eBooks · 2022
Typebook
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmbodied cognitionAestheticsSAINTSociologyHistoryEpistemologyPhilosophyArt history

Abstract

fetched live from OpenAlex

In our age of biomedicine, society often treats sickness and disability as problems in need of solution. Phenomena of embodied difference, however, have not always been seen in terms of lack and loss. This book explores the case of early modern Catholic Canada under French rule and shows it to be a period rich with alternative understandings of infirmity, disease, and death. Counternarratives to our contemporary assumptions, these early modern stories invite us to creatively imagine ways of living meaningfully with embodied difference today. At the heart of the account are a range of historical sources: Jesuit stories of illness in New France, an account of Canada's first hospital, the hagiographic vita of Catherine de Saint-Augustin, and tales of miraculous healings wrought by a dead Franciscan friar. In an early modern world that subscribed to a Christian view of salvation, both sickness and disability held significance for more than the body, opening opportunities for virtue, charity, and even redemption. The book demonstrates that when these reflections collide with modern thinking, the effect is a certain kind of freedom to reimagine what sickness and disability might mean to us. Reminding us that the meanings we make of embodied difference are historically conditioned, the book makes a forceful case for the role of history in broadening our imagination.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.015
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0240.007

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 head0.038
GPT teacher head0.199
Teacher spread0.161 · 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 source (direct Gemma or distilled Codex), not a consensus.

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
Published2022
Admission routes1
Has abstractyes

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