Digital Text Analysis and Early Shakespeare Bibliography: Using Voyant Tools with Bad OCR
Bibliographic record
Abstract
This is an accepted article with a DOI pre-assigned that is not yet published.Enumerative bibliographies are lists of scholarship that capture the state of a field. This article first evaluates digital texts of one such bibliography, Franz Thimm’s Shakspeariana from 1564-1864 (1872), before applying textual analysis using Voyant Tools. The takeaways are both methodological and interpretive: we can use inaccurate online texts (“dirty OCR,” that is, optical character recognition), we can fruitfully apply text analysis to printed bibliographies, and we can learn about bibliographies with Voyant Tools even if they are multilingual. This research shows how Thimm’s bibliography emphasizes Shakespeare publication from major urban centres and surfaces the importance of nineteenth-century German translation and scholarship on Shakespeare, while inviting us to reconsider how we credit translators (or not) as we name them in our lists. Ultimately, experimenting with digital tools to analyze early bibliographies can help us better understand the history of our scholarship.
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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 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".