Bibliographic record
Abstract
The scenarios for journal articles that contain more than one language are no longer (and never really were) limited to having an article’s title, abstract, and keywords translated to additional languages. Journal Article Tag Suite (JATS) currently has a variety of structures for tagging articles that are in multiple languages or have substantial amounts of content in more than one language. However, these structures are not all coherent and are not up to the tasks of handling some common use cases. A subcommittee of the National Information Standards Organization (NISO) JATS Standing Committee (with participation from members of the Standards Tag Suite (STS) and Book Interchange Tag Suite (BITS) committees and some other invited experts) was formed, in 2021, with the goal of recommending changes to JATS to enable it to usefully encode multilingual articles. The subcommittee has recommended a set of changes that introduce new structures that can be available to JATS users who need them while minimizing the burden JATS users who rarely deal with multilingual content. Most of these changes are backward compatible with earlier versions of JATS. These changes are currently a work in progress and may become available in a future version of JATS. This paper presents a proposal for improving JATS to better support tagging multilingual articles with the hope of garnering feedback and suggestions from the JATS community.
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 | 0.045 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.041 |
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".