MétaCan
Menu
Back to cohort
Record W2005766429 · doi:10.1080/17498430.2015.1004895

Mathematicians and the early English life insurance industry

2015· article· en· W2005766429 on OpenAlexaff
David R. Bellhouse

Bibliographic record

VenueBSHM Bulletin Journal of the British Society for the History of Mathematics · 2015
Typearticle
Languageen
FieldMathematics
TopicProbability and Statistical Research
Canadian institutionsWestern University
Fundersnot available
KeywordsLife insuranceBusinessInsurance industryActuarial scienceHistoryLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Motivated by a manuscript find in the Macclesfield Collection held by Cambridge University Library, the role of mathematicians in the emerging life insurance industry in eighteenth-century England is examined. Two early life insurance societies are examined here in detail. In one, a prominent mathematician's arguments were ignored thus confirming the generally accepted view among historians of insurance that the role of mathematicians at this time was minor or non-existent. For the other case, the promoter of two insurance schemes used his mathematical knowledge to design the operation of his insurance plans thus showing that mathematical activity was at least not non-existent. The manuscript find still leaves the question of what the motivation and impact was of the major writers on life annuities during the first half of the eighteenth century. This is addressed by considering the major economic background of early eighteenth-century England—land and property.

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)0.002
metaresearch head score (Gemma)0.004
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.009
Scholarly communication0.0030.004
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.076
GPT teacher head0.283
Teacher spread0.207 · 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 designTheoretical or conceptual
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

Citations2
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueBSHM Bulletin Journal of the British Society for the History of MathematicsSame topicProbability and Statistical ResearchFrench-language works237,207