Lynx D2.5 Report on Lynx acquired vocabularies
Bibliographic record
Abstract
This deliverable constitutes the final report on the vocabulary resources collected and generated in the Lynx project, coverןמע both general language and legal terminology. It is part of Work Package 2, which is concerned with the acquisition and management of linguistic data for Lynx, and includes the process followed for )a) the identification of existing language resources (corpora, dictionaries, glossaries, thesauri, terminologies, ontologies) of interest for the domains covered in the project; (b) the adaptation of resources coming from Lynx partners; and )c) the creation of language resources to cover the specific needs of Lynx use cases (when such data sources did not exist or were not freely available). Both types of legal and general vocabularies play a vital role in the Legal Knowledge Graph that is at the heart of Lynx, to enable and facilitate its multilingual services. The existing vocabularies ensue mainly from two Lynx partners (TILDE and KD), along with some open access terminological resources in particular, and are complemented by resources specifically created to meet business case needs. This process has met with considerable success so far, as shown by the number and quality of vocabularies reported upon in this document, and will be enhanced, if necessary, in the context of WP3 (service development) and WP5 (pilots), where language resources are going to be used. The aim is to further reinforce, enhance and improve the legal knowledge services provided by Lynx, and align more closely with its three use case partners. The Lynx partners would thus be able to offer and assure yet higher standards for its linguistic infrastructure regarding both general language and legal terminology.
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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.076 |
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".