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Record W4245601178 · doi:10.1017/cnj.2016.23

Subgrouping of Coahuitlán Totonac

2016· article· en· W4245601178 on OpenAlexaff
Devin Moore

Bibliographic record

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsLexiconFamily treePhonologyLanguage familyTree (set theory)LinguisticsHistorical linguisticsGeographyGenealogySociologyHistoryMathematicsCombinatoricsPhilosophy

Abstract

fetched live from OpenAlex

Abstract Coahuitlán Totonac is spoken in Veracruz, Mexico, and has been variously ascribed to two different branches of the Totonacan family tree. While recent work has begun to bring empirical evidence to the internal structure of this family tree, there remain several important areas of disagreement, in addition to the disputed affiliation of Coahuitlán. This article informs the family tree and demonstrates that Coahuitlán belongs to the Northern branch using shared innovations and two computational methods. The comparative method seeks sets of shared innovations for evidence of subgrouping. This article presents proposed shared innovations in phonology, morphology, and lexicon, which fall into two sets, one belonging to the Sierra and Lowland branches, and the other belonging to the Northern. Coahuitlán Totonac overwhelmingly shares innovations found in Northern languages and lacks innovations found in Sierra. Two quantitative methods are also used to show that Coahuitlán groups groups closely with other Northern languages.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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 designObservational
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

Citations1
Published2016
Admission routes1
Has abstractyes

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