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Record W4393371867 · doi:10.1109/mahc.2024.3364900

Was There a French Engine Before Babbage’s Difference Engine?

2024· article· en· W4393371867 on OpenAlexaff
Marc LaViolette

Bibliographic record

VenueIEEE Annals of the History of Computing · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophy, Science, and History
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsHistory of computingSteam engineComputer scienceEngineeringAutomotive engineeringManufacturing engineeringOperating systemHistoryAeronauticsMechanical engineering

Abstract

fetched live from OpenAlex

Most writings discussing Charles Babbage’s attempts at creating mechanical calculating machines use the heroic theory of invention and scientific development as a hypothesis for describing his accomplishments. Many of the mechanisms he imagined, do appear to be the first implementations of ideas used by modern computing machines such as pipelining, parallel computing, anticipating carry, mill (cpu), and store (RAM) (see Bromley [1] and Swade [2]).

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)0.010
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.016
Scholarly communication0.0080.016
Open science0.0010.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0140.005

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 head0.068
GPT teacher head0.248
Teacher spread0.180 · 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 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

Citations13
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Annals of the History of ComputingSame topicPhilosophy, Science, and HistoryFrench-language works237,207