The Adaptive TEI Network: Antiracist, Decolonial, and Inclusive Markup Interventions
Bibliographic record
Abstract
This poster presentation introduces the “PhD CoLab” project (University of British Columbia, 2024-26, with the collaboration of the SFU Digital Humanities Innovation Lab, DHIL) which brings together graduate students, faculty, and staff from various fields in humanities, languages, and literatures. While based in Vancouver, an English-speaking North American education system, this multifaceted project is concerned with the continuities and limitations of text encoding across languages (English, Spanish, German and Russian), geographic regions, and literary genres. This poster will provide concrete examples to illustrate the larger objectives of the PhD CoLab. One of the encompassed projects is NovElla, which focuses on making visible and accessible short prose fiction written by early modern Spanish writers. It includes a catalog of annotated bibliographic resources to help promote future research by both students and scholars. Another example, related to Latin America, is Unión Cívica Project that focuses on the newspaper Unión Cívica published by the eponymous political movement founded in 1961 in the aftermath of the Rafael L. Trujillo dictatorship (1930-1961) in Dominican Republic. We will offer high resolution digital reproductions of 140 issues, with annotations, to provide political and historical context. Furthermore, the very structure of the Adaptive TEI Network, rooted in a team-oriented ethos, disrupts the traditional mode of solitary, humanistic research. PhD students collaborate in a transdisciplinary team-based, project-oriented environment where we learn from and with one another while we propose a new TEI schema for text-encoding projects that consider antiracist, decolonial, inclusive and feminist markup practices. In short, the TEI schema aims to address some of the projects’ research questions like: Can we adapt current TEI modules or does an antiracist/decolonial and feminist engagement with the literary text necessitate new TEI markup standards or new modules? Is the TEI also robust enough to address/function for multilingual texts?
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".