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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.028 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".