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Record W2613290787 · doi:10.32396/usurj.v3i2.231

Match-Making in Britain from 1827 to 1910: The Dangers of White Phosphorus in Lucifer Match Production

2017· article· en· W2613290787 on OpenAlexaffvenue
Elise Lehmann

Bibliographic record

VenueUSURJ University of Saskatchewan Undergraduate Research Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicHistorical and Scientific Studies
Canadian institutionsUniversity of SaskatchewanUniversity of Alberta
Fundersnot available
KeywordsWhite (mutation)Government (linguistics)PhosphorusWhite paperProduction (economics)White PhosphorusBusinessLawPolitical scienceEconomicsChemistryPhilosophy

Abstract

fetched live from OpenAlex

Throughout the nineteenth century the British match-making industry used white phosphorus in the production of lucifer matches, despite the knowledge taht the chemical could cause a deadly disease known as phosphorus necrosis. Until the 1890s, due to cover-ups made by match-making companies, the British government was unaware of the scale of phosphorus necrosis cases and had been led to believe that the chemical was being used safely. However, even after journalists exposed the truth, the British government and match-making companies were still unwilling to ban white phosphorus because of the economic and social consequences of shutting down lucifer match production. It was not until a chemical alternative was found that both the match-making industry and the British government were prepared to ban the use of white phosphorus.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.389

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.012
Scholarly communication0.0030.001
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.061
GPT teacher head0.314
Teacher spread0.253 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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