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Record W6906375770 · doi:10.17169/langsci.b81.146

The future of dialects

2016· other· en· W6906375770 on OpenAlexaboutno aff

Bibliographic record

VenueUniversitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver · 2016
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsDialectologyGermanSelection (genetic algorithm)Identity (music)SociolinguisticsFocus (optics)Germanic languagesFunction (biology)

Abstract

fetched live from OpenAlex

Traditional dialects have been encroached upon by the increasing mobility of their speakers and by the onslaught of national languages in education and mass media. Typically, older dialects are “leveling” to become more like national languages. This is regrettable when the last articulate traces of a culture are lost, but it also promotes a complex dynamics of interaction as speakers shift from dialect to standard and to intermediate compromises between the two in their forms of speech. Varieties of speech thus live on in modern communities, where they still function to mark provenance, but increasingly cultural and social provenance as opposed to pure geography. They arise at times from the need to function throughout the different groups in society, but they also may have roots in immigrants’ speech, and just as certainly from the ineluctable dynamics of groups wishing to express their identity to themselves and to the world. The future of dialects is a selection of the papers presented at Methods in Dialectology XV, held in Groningen, the Netherlands, 11-15 August 2014. While the focus is on methodology, the volume also includes specialized studies on varieties of Catalan, Breton, Croatian, (Belgian) Dutch, English (in the US, the UK and in Japan), German (including Swiss German), Italian (including Tyrolean Italian), Japanese, and Spanish as well as on heritage languages in Canada.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.005
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.013

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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