Bibliographic record
Abstract
Abstract This article describes an approach to the treatment of texts in complex large textual traditions. Editors are interested in the text as it appears line-by-line in each document, and in how the versions of the text differ from document to document. It is useful to define a text as the record of an act of communication, inscribed in a document: thus, the instance of the act of communication we identify as Geoffrey Chaucer’s Canterbury Tales, as it appears in the Hengwrt manuscript. In this view, every text has a dual aspect: it is both the words as they are inscribed in a particular document, and as they constitute an act of communication and its parts. This presents challenges for scholars who wish to record both aspects. In encoding implementations, these two aspects are commonly treated as ‘overlapping hierarchies’. However, the ‘overlapping hierarchy’ model does not deal with cases where text segments are not contiguous in either aspect and cannot overlap cleanly. To meet these cases, the Textual Communities project developed an architecture in which the two aspects are represented as distinct and independent hierarchies (trees), with text segments referenced to nodes on each tree. The linking of text segments to the two trees is managed by a JSON database, accessed through transcription and collation tools presented in a Web interface. Textual Communities does not implement the whole of this architecture in terms of validation, ingestion, and processing. Full exploration and implementation of the architecture here described are challenges for future scholars.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".