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2021· book-chapter· en· W3136280194 on OpenAlexaff
Paul Delany

Bibliographic record

VenueAdvances in human resources management and organizational development book series · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPublishingNarrativeFace (sociological concept)Promotion (chess)Construct (python library)Media studiesPandemicArtArt historyCoronavirus disease 2019 (COVID-19)HistoryAdvertisingSociologyLiteraturePolitical scienceComputer scienceSocial scienceBusinessLawPoliticsMedicine

Abstract

fetched live from OpenAlex

Some might think that the COVID-19 pandemic changes little, because novelists have always worked from home and found isolation to be necessary for their creative process. But in fact, there is a spectrum in sites of production. Even in a novelist's solitary study, her task is to construct a narrative out of her past social experience. The pandemic has caused a drastic reduction in such face-to-face activities. Changes in the consumption of fiction during the pandemic can be tracked on Amazon and include a shift from print to Kindle, and also shifts in genres. The biggest gain in share on Kindle has been in children's books. Post-publication activities have become steadily more important, as promotion of a book is now integral to its production. The rise of self-publishing on Kindle Direct Publishing bypasses traditional gatekeepers. With KDP almost everyone now can have their own press, and the centres of gravity of publishing have moved from London and New York to Seattle. Post-apocalyptic fiction envisions life in the wake of natural disasters.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.523
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0070.007
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.5230.362

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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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