"Contributing to Most Things": Richard Marsh, Literary Production, and the Fin de Siècle Periodicals Market
Bibliographic record
Abstract
Franco Moretti has recently called for a "distant reading" which "focus[es] on units that are much smaller or much larger than the text: devices, themes, tropes—or genres and systems." For Moretti, such a reading involves a "process of deliberate reduction and abstraction," which produces "artificial constructs—graphs, maps, trees" and results in " a specific form of knowledge " quite different from "traditional" close readings of canonical literature. This article builds on Moretti's work, offering a statistical case study of the professional practice—production rates, generic choices, and writing patterns—of the bestselling popular author "Richard Marsh" (1857-1915). While the resulting graphs indicate patterns and trends in the career of one professional writer, their abstract nature also suggests ways in which we might approach the work of other authors, indeed, authorship itself, through statistical analysis.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".