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Record W4221096862 · doi:10.1111/hic3.12720

Novels and newspapers in Piketty's <i>Capital and Ideology</i>

2022· article· en· W4221096862 on OpenAlex
Heidi Tworek

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHistory Compass · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Influence and Politics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNewspaperIdeologyEliteGuard (computer science)Capital (architecture)SociologyMedia studiesHistoryAestheticsPolitical sciencePoliticsLawArtComputer science

Abstract

fetched live from OpenAlex

Abstract This article explores the use of textual sources in Thomas Piketty's Capital and Ideology as seriously as others have examined Piketty's use of statistics. Although a commendable attempt to engage with non‐quantitative sources, the book focuses on elite novels, selects works unsystematically, and takes an old‐fashioned approach to media. Ironically, Piketty's use of literature perpetuates the same focus on the upper classes that he wishes to guard against. In this response, I suggest how a book on capital and ideology might examine novels and newspapers rigorously. First, I look at how a broader understanding of literary production as a business and a focus on non‐elite books might inform the use of novels. Second, I consider how to employ big‐data techniques to study newspapers. Overall, I argue, taking novels and newspapers seriously shows the importance of non‐elite sources and of incorporating big‐data techniques often pioneered by literary scholars.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.259
Teacher spread0.233 · 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