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Record W7095435360

Trends in Publications Affecting Binding and Conservation

2015· article· en· W7095435360 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical Geography and Geographical Thought
Canadian institutionsnot available
Fundersnot available
KeywordsCraftQuarter (Canadian coin)CommodityProduction (economics)Electric light
DOInot available

Abstract

fetched live from OpenAlex

THEEARLIEST PRINTED books are not easily dis-tinguished from their manuscript predecessors. The printers attempted to imitate in their first type faces the prevalent book hands of their Iocality and used the same papers as those available to the scribes. Binders continued to practice their craft as they had for centuries. The original purpose of bindings was to protect the visible cords and sewings of the books. Since leather was the only suitable material avail-able and a scarce commodity in the 15th century, many manuscripts and incunabula were frequently only half covered with skins, leaving the wooden boards bare. As economic conditions permitted, leather was used to cover the outside completely. Occasionally, other materials found temporary acceptance, but until the first quarter of the 19th century a “bound book ” generally meant one covered in animal skin. “Fine bindings ” or “hand-book bindings ” are still produced today, even in the United States, ’ but they were displaced from the general market by three inventions made between 1820 and 1832: the use of cloth as covering material, the casing-in method and the gold-stamp-ing on cIoth, which made the mass production of books possible. Other inventions helped in the establishment of mass-production methods. Earl Stanhope invented the iron hand press in 1798 which was later improved by the cylinder press of Konig in 1814. The stereo-type plates of William Ged were used in the United States around 1812, the Fourdrinier paper-making machine made its American ap-pearance in 1827, and William Church‘s composing machine was in use here by 1830.2From these early beginnings the industry developed over the next one-hundred years by adding refinements to its processes. Mechanization was introduced during the second half of the 19th century, but basically the changes were minor. The book-cloth used during the 19th century was usually drab in color and variations came

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)0.004
metaresearch head score (Gemma)0.023
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 score0.101
Threshold uncertainty score0.339

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.023
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0140.042
Science and technology studies0.0030.004
Scholarly communication0.0200.010
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1010.028

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 head0.336
Teacher spread0.265 · 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
Published2015
Admission routes1
Has abstractyes

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