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Record W2235307518 · doi:10.1080/13555502.2015.1118851

Shattered Minds: Madmen on the Railways, 1860–80

2016· article· en· W2235307518 on OpenAlexaff
Amy Milne‐Smith

Bibliographic record

VenueJournal of Victorian Culture · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsHistoryArt historyArtMedia studiesSociology

Abstract

fetched live from OpenAlex

Scholars have long pointed to stories of death and disaster on the railways as proof of profound Victorian anxieties about technology. And yet the traumatic crash was not the only anxiety revealed by sensational railway stories. In the 1860s, a surprising number of newspaper accounts emerged telling tales of ordinary men losing their minds on the railways. These stories were told and retold across the periodical press, exaggerating both the extent of the problem and the severity of the danger for the everyday traveller. Analysing a broad range of press accounts and government policy, this article traces a moral panic in the making. These stories reveal a great concern about the seeming fragility of the male mind when exposed to the modern, industrial world. As this article demonstrates, fears of madness were not limited to Lunacy Commissioners and alienists; they were in fact a staple of popular culture. If a railway journey was all it took to drive a seemingly sane man to madness, what did that say about the health of British manhood?

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score0.931

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.005
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.026
GPT teacher head0.300
Teacher spread0.275 · 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 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

Citations7
Published2016
Admission routes1
Has abstractyes

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