The Romanian linguistic cartography in the digitizing era: the electronic atlases
Bibliographic record
Abstract
Abstract The three series of national linguistic atlases (WLAD, ALR and NALR) proof that the Romanian linguistic cartography has one of the richest and most important traditions in Europe, fact acknowledged by the linguistic community starting from the first half of the last century. This tradition continues nowadays with the digitization of the linguistic atlases. The first achievement in this direction is the release in 2007 of the third volume from series NALR. Moldavia and Bukovina with the help of bespoke software built in collaboration by linguists and computer scientists from Iasi Branch of the Romanian Academy. Another project in the same field, done by a Romanian-Canadian team, is the Online Romanian Dialect Atlas which plans to build an interactive database for dialects using a multidimensional scaling statistical technique. The Bukovina Audiovisual Linguistic Atlas (ALAB) is the most recent project for a digital atlas of the Romanian academic community and it is based in the research centre from Iasi. The ALAB project, started in 2010, plans to build, for the first time in Romania, an audio-video atlas centred on sociolinguistic features. This atlas will present, with the help of online support in one interface, diatopic, diastratic (diasexual and diagenerational), and possible diaphasic variations on dialect level.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".