Chance Encounters: World Literature Between the Unexpected and the Probable
Bibliographic record
Abstract
This essay brings probabilistic reasoning into concerted dialogue with book-historical and sociological approaches to world literature. Using extensive bibliographic data about literary translations into Japanese during the modern era, it develops a series of case studies at interrelated scales—the literary anthology, world library collections, and individual readers—to reason about the likelihood of certain authors or works being plucked from the swirling currents of the global traffic in books. At each scale, I consider how such data might inform the interpretations we give to the choice of one author over another in a given context. Woven into these case studies is an extended reflection on the history of probabilistic reasoning from the late-eighteenth century to the late-twentieth. What, this essay ultimately asks, might literary historians gain from taking this history seriously in our own appeals to chance as a form of historical explanation?
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.017 | 0.043 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".