Cultural Capitals: Modeling Minor European Literature
Bibliographic record
Abstract
Conceived against the backdrop of ongoing debates regarding the status of national literary traditions in world literature, this essay offers a computational analysis of how national attention is distributed in contemporary fiction across multiple national contexts. Building on the work of Pascale Casanova, we ask how different national literatures engage with national themes and whether this engagement can be linked to one's position within a global cultural hierarchy. Our data consists of digital editions of 200 works of prize-winning fiction, divided into four subcorpora of equal size: U.S.-American, French, German, and a collection of novels drawn from 19 different "minor" European languages. We ultimately find no evidence to support Casanova's theory that minor literatures are more nationalistic than literature produced within major cultural capitals. Indeed, the evidence points to the exact opposite effect: all three of the models we employ suggest that novels written in more minor languages tend to be significantly less nationalistically focused than those written in European centres like France or Germany. Nevertheless our data do confirm Casanova's larger hypothesis of the existence of visible stylistic effects associated with a book's location within a global cultural hierarchy of languages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".