Bibliographic record
Abstract
Digital Humanities (DH) and Digital Library (DL) projects are complex systems that require specialized programming skills. Many encoders cannot take their work to the next level by transforming their collections of structured XML texts into a web searchable and browsable database. Often teams of text encoders are able to encode their texts with a high degree of sophistication, but unless they have funds to hire a programmer, their collections far too often remain on local disk storage away from public access. aims to relieve some of this burden by providing the tools to manage an extensible, modular and configurable XML-based repository which will house, search, browse, and display documents encoded in TEI-Lite on the world wide web. provides an administrative interface that allows DL and DH administrators to upload and delete documents from a web accessible repository; analyze XML documents to determine elements for searching and browsing; refine ontology development; select inter and intra document links; partition the repository into collections; create backups; generate search, browse, and display pages; customize the interface; and associate XSL transformation scripts and CSS stylesheets to obtain different target outputs (HTML, PDF, etc.).
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.202 | 0.217 |
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".