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Record W4389139366 · doi:10.1163/22105832-bja10030

Morphological diffusion and the internal subgrouping of Central Totonac

2023· article· en· W4389139366 on OpenAlexafffund
David Beck

Bibliographic record

VenueLanguage Dynamics and Change · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaEndangered Languages Documentation Programme
KeywordsGeographyOutlierSet (abstract data type)Distribution (mathematics)Division (mathematics)GenealogyHistoryLinguisticsComputer scienceMathematicsArtificial intelligenceArithmetic

Abstract

fetched live from OpenAlex

Abstract The Totonac branch of the Totonacan (also known as Totonac-Tepehua) family is traditionally broken down into four divisions—Misantla, Northern, Sierra, and Lowland. Misantla is an obvious outlier, but the relationship among the remaining three, which comprise the Central Totonac division, is uncertain due to competing lines of evidence: lexical isoglosses group Sierra and Lowland against Northern while morphological changes appear to set Sierra off against the other two. The spatial distribution of the morphological innovations shows these not to be a coherent set of changes inherited from a common ancestor, but instead a series of successive innovations diffused in a wave-like pattern. This paper also demonstrates that the morphological innovations are more recent than the lexical changes, supporting the prior separation of Sierra-Lowland languages from Northern. The paper also explores the methodological issues associated with the classification of languages in close contact at shallow time depths.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.004
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.293
Teacher spread0.271 · 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 designQualitative
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

Citations3
Published2023
Admission routes2
Has abstractyes

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