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Record W1964178488 · doi:10.1353/bhm.2007.0124

The Doctor Was Surprised; or, How to Diagnose a Miracle

2007· article· en· W1964178488 on OpenAlexaff
Jacalyn Duffin

Bibliographic record

VenueBulletin of the history of medicine · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Medicine Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsMiracleSurpriseMeaning (existential)SemioticsMedical diagnosisHistoryMedical recordLiteraturePhilosophyMedicinePsychologyTheologyEpistemologyArtSurgerySocial psychologyPathology

Abstract

fetched live from OpenAlex

A survey of more than six hundred miracle records in the canonization files of the Vatican Secret Archives from the seventeenth century to the twentieth century reveals that more than 95 percent are healings from illness. The history of the canonization process is summarized to explain the sources. The diagnoses amenable to miracle cure change through time to reflect current medical preoccupations and methods. Physician testimony has always been crucial to the investigation of miracles for declaring the hopeless prognosis and the surprise at recovery. From this analysis, medicine and religion emerge as parallel semiotic endeavors, using their canons of wisdom and careful observation to derive meaning in suffering.

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)0.003
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.015
Scholarly communication0.0040.006
Open science0.0000.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.041
GPT teacher head0.238
Teacher spread0.197 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations17
Published2007
Admission routes1
Has abstractyes

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