TRIPLE Open Science Training Series: Multilingual Vocabularies for SSH (20 April 2022)
Bibliographic record
Abstract
This training event is a synergy of TRIPLE and SSHOC projects, and is devoted especially to the creation, use and management of controlled vocabularies in the SSH. Controlled vocabularies are organized arrangements of words and phrases used to annotate, index and retrieve content through browsing or searching. Multilingualism is an essential feature for the SSH, this is why multilingual SSH vocabularies are greatly needed. The training event provides answers to the following questions, among others: What are SSH Vocabularies and why are they so important? How to create a multilingual SSH Vocabulary (The TRIPLE case)? The large variety of vocabularies and management needs in the SSH. How to build an interoperable infrastructure for vocabularies (The SSHOC case)? 🗓 Date: Wednesday, 20th April 2022, 14.00 – 15.30 (CEST) 🎤 Presenter: Daan Broeder with contributions from other SSHOC partners (CLARIN ERIC/SSHOC PROJECT), Nikos Vasilogamvrakis (EKT) 🤵 Moderator: Iraklis Katsaloulis (EKT/TRIPLE PROJECT) 🏠 Venue: Virtual event via Zoom 📽 Recording: https://youtu.be/3DVsmom4RUk
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.297 | 0.193 |
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".