Analyzing spelling patterns in the manuscripts of the Tales of Canterbury
Bibliographic record
Abstract
Abstract For decades, scholars have suggested that analysis of spellings in medieval European manuscripts might be useful in understanding who wrote the manuscripts and where and when they were written. The increased availability of full-text transcripts of manuscripts is creating larger sets of data and has opened the possibility of using quantitative methods. This article reports on analysis of spellings in manuscripts of Geoffrey Chaucer’s Book of the Tales of Canterbury. The analysis was successful in confirming long-held beliefs, based on traditional paleography, that multiple manuscripts can be identified as written by the same scribe: these manuscripts are closely aligned in their spelling patterns. Further, the analysis showed that manuscripts written by the same scribes might be closely aligned in spellings even though they are copied from exemplars with significantly different texts. The analysis also suggested some unexpected linkages among the manuscripts, which found support in examination of the text in those manuscripts. Three sets of spelling data were submitted to the analysis: one set with all spellings assigned to regularized forms; a second with spellings sorted by headword and part-of-speech; a third with completely unsorted “bags of words” for each document. While the more structured data did yield more granular results, these gains seemed relatively slight compared to the extra effort required to create the data.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".