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Record W4403081422 · doi:10.5539/ass.v20n5p45

Rethinking the Start of Using Textile Fibers

2024· article· en· W4403081422 on OpenAlexvenueno aff
X Zhang, Xiaoming Yang

Bibliographic record

VenueAsian Social Science · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicCrafts, Textile, and Design
Canadian institutionsnot available
FundersPrinceton University
KeywordsTextileBusinessMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Textile historians tend to demonstrate the history of textiles from the start of using fibers yet rarely explain how people knew to use them. With an investigation of the early textile-related remains and a theoretical analysis of the difficulty in and the demand for using fiber materials, a hypothesis is raised that the know-how of using fibers came from the experience of making textiles with both fineness and strength, which was accumulated from the early technologies of interlacing and cord-making. The properties of fiber materials should have been discovered thanks to the frequent use of stripped or sliced materials taken from plants. Among all the main types of the earliest fiber materials used by humans, invisible plant fibers should have been the first to be used, cotton the second, wool synchronous with or a little later than cotton, and silk the last.

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)0.005
metaresearch head score (Gemma)0.005
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: none
Teacher disagreement score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.028
Scholarly communication0.0080.014
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0080.002

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 head0.071
GPT teacher head0.289
Teacher spread0.218 · 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
Published2024
Admission routes1
Has abstractyes

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