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Record W7137722606

Being Modern

2018· other· en· W7137722606 on OpenAlexfundno aff

Bibliographic record

VenueDirectory of Open access Books (OAPEN Foundation) · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersNational Air and Space MuseumQueen's UniversityInstitut Henri PoincaréDepartment of Scientific and Industrial Research, Ministry of Science and Technology, IndiaUniversité de ParisRoyal SocietyImperial College LondonUniversitetet i OsloHarvard UniversityUniversity of East AngliaRoyal Historical SocietyPrinceton UniversityUniversity of CambridgeArts and Humanities Research CouncilYale UniversityUniversity of OxfordDurham UniversitySmithsonian Institution
KeywordsEmblemArchitecturePhenomenonIvory towerCultural phenomenonResource (disambiguation)
DOInot available

Abstract

fetched live from OpenAlex

In the early decades of the twentieth century, engagement with science was commonly used as an emblem of modernity. This phenomenon is now attracting increasing attention in different historical specialties. Being Modern builds on this recent scholarly interest to explore engagement with science across culture from the end of the nineteenth century to approximately 1940. Addressing the breadth of cultural forms in Britain and the western world from the architecture of Le Corbusier to working class British science fiction, Being Modern paints a rich picture. Seventeen distinguished contributors from a range of fields including the cultural study of science and technology, art and architecture, English culture and literature examine the issues involved. The book will be a valuable resource for students, and a spur to scholars to further examination of culture as an interconnected web of which science is a critical part, and to supersede such tired formulations as 'Science and culture'.

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0080.006
Open science0.0000.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0300.008

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 head0.094
GPT teacher head0.419
Teacher spread0.325 · 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
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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