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Introduction

2018· book-chapter· en· W4232182079 on OpenAlexaboutno aff
David Finkelstein

Bibliographic record

VenueOxford University Press eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsCraftFraternityPrint cultureHistory of the bookMedia studiesSpace (punctuation)HistorySociologyVisual artsPolitical scienceArtArt historyLaw

Abstract

fetched live from OpenAlex

This introduction explains the rationale for undertaking this sort of study, and offers a summary of the book. This is an interdisciplinary study of the ‘webs of empire’ that underpinned and enabled skilled print labour networks over the Victorian and Edwardian periods. It is a cultural history, with chapters incorporating original material linked to labour history, working-class literary culture, migration, social history, and print culture. Drawing on a range of unique primary and secondary sources covering Australia, Canada, the UK, Ireland, New Zealand, South Africa, and the United States, it focuses on the ‘typographical web’ that encompassed print economies across these regions. It offers insights into how print culture structures translated across national boundaries, how print workers were mobilized and organized as the century progressed, and how shared craft identities, creative endeavours, and trade press publications created a sense of moral community linking the printing fraternity across space and time.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.619
Threshold uncertainty score0.883

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.3810.197

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 head0.034
GPT teacher head0.233
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2018
Admission routes1
Has abstractyes

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