The McGill library chapbook project: a case study in TEI encoding
Bibliographic record
Abstract
Purpose – The purpose of this case study is to describe a multi-year text encoding initiative (TEI) project that took place in the McGill University Library, Rare Books and Special Collections. Design/methodology/approach – Early nineteenth century English language chapbooks from the collection were digitized, and the proofed text files were encoded in TEI, following Best Practices for TEI in Libraries (2011). Findings – The project coordinator describes the TEI file structure and customizations for the project to support a distinct subject classification of the chapbooks and the encoding of the woodcut illustrations using the Iconclass classification. Research limitations/implications – The authors focus on procedures, use of TEI data elements and encoding challenges. Practical implications – This paper documents the project workflow and provides a possible model for future digital humanities projects. Social implications – The graduate students who participated in the TEI encoding learned a new suite of skills involving extensible markup language (XML) file structure and the application of a markup language that requires interpretation. Originality/value – The McGill Library Chapbook Project Web site, launched in 2013 now provides access to 933 full-text works.
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.019 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".