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Record W2322541727 · doi:10.1515/dialect-2014-0005

The Romanian linguistic cartography in the digitizing era: the electronic atlases

2014· article· en· W2322541727 on OpenAlexaboutno aff
Florin-Teodor Olariu, Veronica Olariu

Bibliographic record

VenueDialectologia et Geolinguistica · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsRomanianAtlas (anatomy)DigitizationLinguisticsBespokeGeographyComputer sciencePolitical scienceTelecommunicationsGeology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.063
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.302
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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