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Record W2760716429 · doi:10.1177/0952695117722716

The unfailing machine

2017· article· en· W2760716429 on OpenAlexaff
Edward Jones‐Imhotep

Bibliographic record

VenueHistory of the Human Sciences · 2017
Typearticle
Languageen
FieldMedicine
TopicHistorical and Scientific Studies
Canadian institutionsYork University
Fundersnot available
KeywordsReignGuard (computer science)Punishment (psychology)SociologyAction (physics)LawPsychologyPolitical scienceSocial psychologyComputer science

Abstract

fetched live from OpenAlex

This article explores how the pre-eminent public psychology of the French Revolution – sentimentalism – shaped the necessity, understanding and construction of its most iconic public machine. The guillotine provided a solution to the problem of public executions in an age of both sentiment and reason. It was designed to rationalize punishment and make it more humane; but it was also designed to guard against the psychological effects of older, more variable and unpredictable methods of public execution on a sentimental public. That public, contemporaries argued, required executions performed by an unfailing technology. Rather than focus on the role of the guillotine after 1793, the article explores how the implacable mechanical action that helped produce the Reign of Terror and multiply the cadavers of medical science was demanded by the guillotine’s origins as a sentimental machine.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.325
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.150
GPT teacher head0.325
Teacher spread0.174 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2017
Admission routes1
Has abstractyes

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