Drupal, TEI and XML: How to prototype a digital humanities tool?
Bibliographic record
Abstract
Purpose This paper aims to take seriously the import accorded to the interface within the digital humanities. It will probe some of the possibilities and limits of the computer interface as a reading and research tool by unpacking theoretical and practical aspects of interface design. Design/methodology/approach The authors wanted to see if they could design a tool that would meet three interrelated goals: the first was to develop a digital tool that would enable scholarship rather than mere publishing. Next, they wanted to build an interface that would acknowledge the situatedness of reading and meaning-making practices. Findings The research-oriented design approach to interface design has shown us how valuable it is to combine research and practice when thinking through issues in the digital humanities. Engaging in such a design project provides the unique opportunity to bring together theoretical concepts relating interface design with robust tools like XML mark-up and Drupal modules. Originality/value There is literature on the subject of transformation of print documents to electronic text (Hayles, 2003) and the representation of text within a computer (Sperberg-McQueen, 1991); this project attempts to build a prototype of what these theories might look like.
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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".