Bibliographic record
Abstract
The main goal of the Text Encoding Initiative (TEI) is the development of a standard for encoding of textual phenomena and manifests itself in the TEI Guidelines and the derived (formal) schemata. For the production and maintenance of these Guidelines and its various file formats, as well as for processing any kind of TEI document, a larger ecosystem of tools, methods, and technical service infrastructure have evolved around the TEI standard. Since the most common serialization format for TEI documents is XML, a lot of generic XML tools are employed, e.g., Apache ANT as a build tool, Saxon as XML processor, and XSLT and XQuery as scripting and query languages. In fact, the most prominent piece of software that the TEI Consortium produces are the TEI Stylesheets, a set of XSL stylesheets that convert to and from TEI files. Supported import and export formats include docx, markdown, HTML, PDF, and many more. But the TEI Stylesheets not only convert ‘regular’ TEI documents but also TEI ODD customization files, acting as an ODD processor to produce both documentation and schemata from an ODD customization—again in various possible output formats. The TEI stylesheets are at the core of most available TEI transformation services including the OxGarage RESTful web service, the oXygen TEI frameworks, the MEI Customization Service, and the TEI Jenkins continuous integration servers which test the TEI build process. Other services aim at providing an online editor for TEI ODD customizations rely on the aforementioned OxGarage services to handle these transformations e.g., produce JSON serializations of the TEI specifications for their internal data structures, and return schemata and documentation to the user. The whole multitude of tools and services can best be understood by looking at the TEI Git repositories that are hosted at GitHub under https://github.com/TEIC. The most prominent are the TEI Guidelines and Stylesheets themselves, while CETEIcean is another rising star. CETEIcean is a pure CSS and Javascript renderer for TEI files which facilitates not only the display, but also the online editing of TEI documents. It is a smart front end library which can be used in one’s own project by simply including the needed javascript and CSS files. Most of the aforementioned tools depend on other services and software. It is the aim of the poster to illustrate these dependencies and to give a thorough overview of the tools and services (actively) maintained by the TEI Consortium. Such an overview would help to distinguish the various tools and services for those members of the TEI community, who find it hard to know what the relationships are between Roma, OxGarage, the Stylesheets, and the Guidelines. While the more tech savvy members would hopefully appreciate these insights for installing and running these services on their own hardware. Finally, the TEI Consortium itself would benefit from better documentation of their service architecture and the feedback of their users gained by this poster.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.287 | 0.372 |
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".