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Record W4297047343 · doi:10.1007/978-981-19-1983-1_1

Genealogy of Artificial Beings: From Ancient Automata to Modern Robotics

2022· book-chapter· en· W4297047343 on OpenAlexaff
Nicolas Reeves, David St-Onge

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsRoboticsArtificial intelligenceAutomatonWeavingPeriod (music)Field (mathematics)EngineeringComputer scienceRobotArtMathematicsAestheticsMechanical engineering

Abstract

fetched live from OpenAlex

This chapter is an extensive overview of the history of automata and robotics from the Hellenistic period, which saw the birth of science and technology, and during which lived the founders of modern engineering, to today. Contemporary robotics is actually a very young field. It was preceded by a 2000-years period in which highly sophisticated automata were built for very different purposes—to entertain, to impress or to amaze—at different times. You will see that the methods and techniques that were used to build these automata, and that largely contributed to the development of robotics, were at times imported from unexpected fields—astronomy, music, weaving, jewellery; and that the impulse that drove automata makers to build their artificial beings was far from rational, but rather rooted in the age-old mythical desire to simulate, and even to realize, an entity from inert materials.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0020.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.027
GPT teacher head0.221
Teacher spread0.194 · 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 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

Citations4
Published2022
Admission routes1
Has abstractyes

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