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Record W746073689 · doi:10.4000/belphegor.586

A Romance of Business: Genre, Scarcity, and the Businessman in the American Economic Novel

2015· article· en· W746073689 on OpenAlexvenueno aff
Jason Douglas

Bibliographic record

VenueBelphégor · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeScarcityRomanceProfitability indexSociologyEconomicsMarket economyLiteratureFinanceArt

Abstract

fetched live from OpenAlex

The subtitle of Richard Kimball’s novel, Undercurrents of Wall Street, represents the central tension of the American business novel that emerges during the second half of the nineteenth century. The novel calls itself as a “romance of business.” As a romance, there is never any doubt that the story must restore the fortunes lost by the main character when his business fails. As a business narrative, a large part of the text explores the myriad ways in which market conditions prevent his return to profitability. The tension between the desire to make him a wealthy man and the difficulty of making him a successful businessman is the kind of tension that becomes the defining feature of the business novel. These novels, as economic narratives, have a logical commitment to market and resource constraints. But as novels, such texts are also committed to reinforcing the relationship between moral behavior and personal success. These novels reflect a dramatic shift in the conceptual landscape that transformed economics from a science of wealth to a science of scarcity. This conceptual shift is reflected in the structural reorganization of the business world to favor a new class of professional managers. The businessman becomes an important financial technology as well as the central element of economic narratives. The economic novel must be understood as an attempt to reconcile the mathematics and calculation of economics as a rising science of scarcity with the genre constraints of the American romance.

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 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.052
GPT teacher head0.235
Teacher spread0.182 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2015
Admission routes1
Has abstractyes

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