Italian Language Resources. From CLARIN-IT to the VLO and Back: Sketching a Methodology for Monitoring LRs Visibility
Bibliographic record
Abstract
This paper sketches a user-oriented, qualitative methodology for both (i) monitoring the existence and availability of language resources relevant for a given CLARIN national community and language and (ii) assessing the offering potential of CLARIN, in terms of Language Resources provided to national consortia. From the user perspective, the methodology has been applied to investigate the visibility of language resources available for Italian within the CLARIN central services, in particular the Virtual Language Observatory. As a proof-of-concept, the methodology has been tested on the resources available through the CLARIN-IT data centres, but, ideally, it could be applied by any national data centre aiming to assess the existence of LRs in CLARIN for any given languages and check their accessibility for the interested users. It is thus argued that such an assessment might be a useful instrument in the hands of national coordinators and centre managers for (i) bringing to the fore both strengths and critical issues about their data providing community and (ii) for planning targeted actions to improve and increase both visibility and accessibility of their LRs.
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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 | 0.022 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".