A Macron Signifying Nothing: Revisiting The Canterbury Tales Project Transcription Guidelines
Bibliographic record
Abstract
The original transcription guidelines of The Canterbury Tales Project were first developed by Peter Robinson and Elizabeth Solopova in 1993. Since then, the project has evolved and expanded in scope, bringing about numerous changes of varying degrees to the process of transcription. In this article, we revisit those original guidelines and the principles and aims that informed them and offer a rationale for changes in our transcription practice. We build upon Robinson and Solopova’s assertion that transcription is a fundamentally interpretive act of translation from one semiotic system to another and explore the implications and biases of our own position (e.g. how our interest in literature prioritizes the minutiae of text over certain features of the document). We reevaluate the original transcription guidelines in relation to the changes in our practice as a means of clarifying our own position. Changes in our practice illustrate how the project has adapted to accommodate both necessary compromises and more efficient practices that better reflect the original principles and aims first laid down by Robinson and Solopova. We provide practical examples that demonstrate those same principles in action as part of the transcription guidelines followed by transcribers working on The Canterbury Tales Project. Rather than perceiving this project as producing a definitive transcription of The Canterbury Tales, we conceptualize our work as an open access resource that will aid others in producing their own editions as we have done the heavy lifting of providing a base text.
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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 | 0.099 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.031 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".