`Creation from nothing': a foregrounding study of James Joyce's drafts for Ulysses
Bibliographic record
Abstract
In May 2002, previously unknown early drafts of what was to become the third chapter of James Joyce's Ulysses surfaced. Joyce worked these drafts into the manuscript that is now known as the `V.A.3-Buffalo' manuscript, in its turn the antecedent of the fair copy forming part of the so-called Rosenbach manuscript on which the published Gabler edition of Ulysses is based. In the study presented in this article, I chose three passages from the earliest drafts and found their corresponding passages in the V.A.3-Buffalo manuscript, and the published text (amounting to a total of nine text passages). In both manuscripts the first layer (before revisions) was chosen, to have the greatest possible difference between versions. After dividing the texts into roughly sentence-length segments, I conducted a foregrounding analysis on all segments of the nine texts and quantified the foregrounding devices. The objective of the presented study was to investigate whether results of past empirical studies of foregrounding, which have concentrated on poems or fairly straightforward narratives would hold true for such complex texts as James Joyce's Ulysses. To measure reader responses to the foregrounding of the texts, ratings on strikingness and affect, as well as reading times per segment were recorded. These responses proved to correlate significantly with the numerical foregrounding index per segment. Additionally, a salient framework is proposed for the study of manuscript materials.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".