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Record W4385277893 · doi:10.3828/catr.37.15

LA NACS COM A INSTITUCIÓ DINAMITZADORA DEL ESTUDIS DE CATALANISTICA: REVISIÓ DEL PASSAT I EL PRESENT AMB LA MIRADA CAP AL FUTUR

2023· article· ca· W4385277893 on OpenAlexaboutno aff
Lourdes Manyé

Bibliographic record

VenueCatalan Review · 2023
Typearticle
Languageca
FieldArts and Humanities
TopicSpanish Linguistics and Language Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCatalanBalearic islandsHumanitiesPolitical scienceLibrary scienceSociologyGeographyArtCartographyComputer science

Abstract

fetched live from OpenAlex

From its foundation in 1978, the North American Catalan Society (NACS) was committed to promoting Catalan studies in North America while establishing connections with academic institutions in Catalan speaking countries. This first group of professors fostered collaboration among NACS members in different geographical areas and this interconnection resulted in fruitful collaborations among university scholars and doctoral students from the United States, Canada, Catalonia, Valencia and the Balearic Islands. The founders also established collaborative agreements with Catalan institutions like the Institut d’Estudis Catalans (IEC) and the Institut Ramon Llull (IRL). Since 1978, those at the helm of the NACS have renewed these agreements, establishing grants to promote the study of Catalan language and culture, and celebrating regular colloquia and symposia at the IEC. The article proposes that future plans should include promoting and disseminating more effectively scholarly studies and publications, and establishing more collaborations among research groups in universities in North America and Catalanspeaking countries.

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)0.012
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.102
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0020.003
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

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 head0.032
GPT teacher head0.311
Teacher spread0.279 · 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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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