Traduire en français le lexique du patrimoine artistique de la ville de Bologne : le sous-corpus comparable BER du projet LBC
Bibliographic record
Abstract
This chapter presents a new tun in the research project Multilingual Cultural Heritage Lexicon that consists in extending the LBC French corpus through a sub-corpus on Bologna and the Emilia-Romagna region (BER). After presenting the two projects connected with the LBC corpus (UniCittà 2019-2021 and UniVOCittà – ongoing), Zotti presents the BER sub-corpus from a quantitative and qualitative point of view reflecting on its role ans its complementarity for the description of the artistic terminology in the LBC comparable monolingual corpus. Zotti concludes by showcasing the applications and results of the corpus-driven approach and its potential for inferring linguistic knowledge when it comes to (diastratic and diatopic) synonymy, suggesting new strategies for the translation of cultural-specific items.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".