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Record W2996698649 · doi:10.22230/cjc.2019v44n4a3723

Distributed Intelligence: Silk Weaving and the Jacquard Mechanism

2019· article· en· W2996698649 on OpenAlexaffvenue
Ganaele Langlois

Bibliographic record

VenueCanadian Journal of Communication · 2019
Typearticle
Languageen
FieldComputer Science
TopicDigital Media and Philosophy
Canadian institutionsYork University
Fundersnot available
KeywordsWeavingMechanism (biology)ProductivityTextileComputer scienceSoftwarePaintingEngineeringVisual artsArtArtificial intelligenceEngineering drawingArchitectural engineeringMechanical engineeringHistoryEpistemologyArchaeologyPhilosophy

Abstract

fetched live from OpenAlex

Background Launched in the early 1800s in Lyon, France, the Jacquard mechanism is often seen as the precursor for today’s software systems, enabling greater productivity in the automated industrial production of woven fabric. Analysis Based on archival research, this media archaeology article argues that the Jacquard mechanism enabled a new form of textile-based digital imaging. Lyon weavers used the mechanism to augment human imagination and strive for increased complexity in their quest for making textiles compete with the dominant media of the time (etching, printing, painting) and with the new medium of photography. Conclusion and implications Such augmentation of human intelligence and imagination brings to light the possibility for alternative relationships between human bodies and brains and digital systems based on collaboration rather than subsumption.

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)0.003
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.011
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0010.001
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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

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