MétaCan
Menu
Back to cohort
Record W6968129748 · doi:10.5281/zenodo.14604271

TEI Technical Infrastructure

2018· article· en· W6968129748 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2018
Typearticle
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsXSLTXMLJSONXQueryInteroperabilityScripting languageMetadata

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.005

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.

Opus teacher head0.021
GPT teacher head0.243
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it