Espais d'intercomprensió en l'àmbit de les llengües romàniques: aprendre simultàniament tres llengües estrangeres i impulsar el plurilingüisme en la comunitat universitària
Bibliographic record
Abstract
Fomentar el plurilingüisme és una manera d'augmentar les competències lingüístiques i personals dels estudiants universitaris que té un alt impacte social. En el cas de les llengües romàniques, quan en parlem una, entendre les altres és molt fàcil si ens centrem primer en la comprensió escrita. Tot l'aprenentatge es basa en el bagatge lingüístic que tenim com a parlants d'una d'aquestes llengües romàniques, com a llengua materna o segona, i en l'alta proximitat d'aquestes llengües entre si.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".