"Pacience is an Heigh Vertu": Managing The Canterbury Tales Project Via Textual Communities
Bibliographic record
Abstract
Any large digital humanities project presents a difficult institutional problem: a small cluster of academics, most likely traditionally trained as independent researchers, can find themselves at the head of a team that closely resembles a small tech startup. At least, this was the experience of The Canterbury Tales Project, Phase 2, with upwards of thirty employees transcribing on an environment under ongoing development; programmers working on that environment ; and senior members of the project promoting that environment and transcription of the Canterbury Tales to other academics internationally through workshops. This article is a reflective essay on the second phase of the Canterbury Tales Project and the various successes and challenges that unfolded throughout that process. Our focus is how the project both managed the transcription team working locally at the University of Saskatchewan and facilitated transcription workshops abroad. We detail our training process and the transcription workflow as facilitated via the Textual Communities environment. We also examine and evaluate the causes of the project’s challenges—often the result of institutional pressures or technological changes—and our reactions to those challenges, emphasizing successful strategies. Finally, we proffer future changes for the project that we believe would have made considerable positive impact if implemented from the outset of phase two and still have potential as helpful resources now. It is our hope that in detailing our process we can help other large DH projects mimic our successes and, perhaps even more importantly, avoid any pitfalls that challenged us.
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 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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.012 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".