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Record W2791656533 · doi:10.1111/amet.12603

Animating creative selves: Pen names as property in the careers of Canadian and American romance writers

2018· article· en· W2791656533 on OpenAlexaboutno aff
Jessica Taylor

Bibliographic record

VenueAmerican Ethnologist · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicShakespeare, Adaptation, and Literary Criticism
Canadian institutionsnot available
Fundersnot available
KeywordsRomanceProperty (philosophy)PublishingAnimationIdeal (ethics)AdvertisingBrand namesSociologyValue (mathematics)LawHistoryLiteratureArtBusinessPolitical scienceComputer sciencePhilosophy

Abstract

fetched live from OpenAlex

ABSTRACT Most romance writers in Canada and the United States in the first decade of the 21st century wrote under at least one, and as many as three or four, pen names. In doing so, they typified the ideal of a middle‐class worker who is both a business and a brand. Yet writers went beyond the singular branded self to develop multiple brand names. Prompted by publishers, writers developed pen names as forms of property that, like brands, could be used to mediate between producer and consumer. These newly developed properties were created according to specifications that reflected the gendered and racialized structure of mass‐market publishing. This process of property creation and animation was imagined to create value for writers and publishers in an uncertain, recession‐era economy. [ authors , branding , gender , labor , romance , United States , Canada ]

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.736

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0300.031
Scholarly communication0.0120.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.041
GPT teacher head0.270
Teacher spread0.229 · 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 designQualitative
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

Citations4
Published2018
Admission routes1
Has abstractyes

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