Metode in orodja za lažjo pripravo korpusov usvajanja jezika
Bibliographic record
Abstract
Prispevek predstavi težka mesta izdelave korpusov usvajanja tujega in maternega jezika, kot so transkribiranje besedil, anonimizacija, ročno označevanje in vsebinsko kategoriziranje popravkov, v nadaljevanju pa novo prosto dostopno orodje, ki ponuja rešitev za opisane metodološke izzive.Orodje, ki temelji na švedskem programu Svala, smo prilagodili za slovenščino, ga nadgradili, da omogoča delo s korpusoma Šolar in KOST, ter evalvirali s pomočjo dejanske korpusne gradnje.korpusi usvajanja jezika, Svala, KOST, Šolar This article highlights the challenges of creating learner and developmental text corpora that feature error corrections: transcription and anonymization of texts, and manual annotation and categorization of corrections.It presents a new freely available tool that offers a solution to these challenges.Based on the Swedish Svala software, the tool has been adapted for Slovenian, modified to work with the Šolar and KOST corpora, and evaluated as part of an actual corpus creation process.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".