Tagging Time and Space: TEI and the Canadian Stratford Festival Promptbooks
Bibliographic record
Abstract
This paper presents the first phase in the development of a new, TEI-based protocol for the encoding of promptbooks. Because the principal function of a promptbook is to record spatiotemporal events whose communicative importance supersedes that of the book in which they are recorded, current standards for digital encoding do not always apply. With the stage managerial artifacts of The. John Gray and the Canadian Stratford Festival Archives as case studies, we provide a rationale for exploring additions to the existing TEI guidelines to account for the unique characteristics of promptbooks. Cet article présente la phase initiale du développement d’un nouveau protocole de codage TEI adopté pour des livres rapides. Puisque la fonction principale d’un livre rapide est l’enregistrement des évènements spatio-temporels dont l’importance communicative l’emporte sur celle du livre dans lequel on les écrit, les normes de codage numérique ne s’applique pas toujours. Avec les artefacts du régisseur The. John Gray et des archives du festival de Stratford du Canada, nous fournissons une justification pour la recherche sur l’adjonction aux consignes de TEI qui rend compte des caractéristiques uniques des livres rapides. Mots-clés: codage de texte; TEI; livres rapides; textes littéraires
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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".