Creating and Implementing an Ontology of Texts, Documents and Works in Complex Textual Traditions
Bibliographic record
Abstract
This article suggests an ontology of texts, documents and works of particular relevance to the editing of complex large textual traditions, such as those of the Greek New Testament (c. 5000 witnesses), Dante's Commedia (c. 800) and Chaucer's Canterbury Tales (88). The need for this ontology is reviewed through a brief history of the Canterbury Tales project's work over three decades, with references also to the Greek New Testament and Commedia editorial projects. The central definition of the ontology is that a text is an act of communication inscribed in a document. Further, both the document and the act of communication may be represented as independent and ordered hierarchies of content objects (hence, trees), with textual nodes appearing on both trees, in different orderings and structures across the two trees. The Textual Communities environment successfully implements parts of the ontology of texts, documents and works to enable data collection, management and publication according to the needs of its current users, demonstrating the considerable advantages of this model for textual processing. However, Textual Communities does not implement the whole of this model in terms of data validation, ingestion and processing. Full exploration and implementation of the model here offered are challenges for future scholars. Successful implementation, however, would have considerable benefits, both for scholars working with complex large traditions, and also for those working with smaller but highly complex document sets, such as authorial manuscripts.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".