TEI Models for the Publication of Social Sciences and Humanities Journals: Opportunities, Challenges, and First Steps Toward a Standardized Workflow
Bibliographic record
Abstract
The TEI Guidelines are developed and curated by a community whose main purpose is to standardize the encoding of primary sources relevant for humanities research and teaching. But other communities are also working with TEI-based publication formats. The first goal of this paper is to raise awareness of the importance of TEI-based scholarly publishing as we know it today. The second goal is to contribute to a reflection on the development of a TEI customization that would cover the whole authoring-reviewing-publishing workflow and guarantee archiving options that are as solid for journal publications as what we now have for primary sources published in TEI.
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 | 0.091 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.042 | 0.044 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".