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Record W4307824511 · doi:10.1075/ivitra.32.02val

History of the group GCS/ASOLC/SOCS

2022· book-chapter· en· W4307824511 on OpenAlexaboutno aff
Francesc Vallverdú

Bibliographic record

VenueIVITRA research in linguistics and literature · 2022
Typebook-chapter
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsGroup (periodic table)Computer scienceEmbedded systemChemistry

Abstract

fetched live from OpenAlex

Abstract In 1973 a small group of scholars from the Catalan Countries joined to form the Catalan Group of Sociolinguistics (GCS). Their mission was to promote the planning of the Catalan language and sociolinguistic studies in this area. Their first public appearance was at the 8th Congress of Sociology (Toronto, 1974), where, thanks to L.V. Aracil – one of the promoters of GCS –, a session on the Catalan language was organised, the material from which was published in 1977 in the first issue of the annual review Treballs de Sociolingüística Catalana. In 1981, GCS had eighteen associates and by 2005, the group’s expansion led to its becoming the Association of Catalan Language Sociolinguists (ASOLC). Shortly after it became a subsidiary of the Institute of Catalan Studies (IEC) and in 2008 it adopted the name Catalan Society of Sociolinguistics (SOCS). Over the years this association has not only been dedicated to promoting and disseminating sociolinguistic research, but it has also been involved in numerous planning initiatives.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.003
Scholarly communication0.0050.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.008

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 head0.060
GPT teacher head0.292
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

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Same venueIVITRA research in linguistics and literatureSame topicHistory of Computing TechnologiesFrench-language works237,207