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Record W4311681239 · doi:10.22215/etd/2022-15205

A Theoretically and Comparatively Informed Description of Yogad Morphology

2022· dissertation· en· W4311681239 on OpenAlexaff
Sarah Koren

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsCarleton University
Fundersnot available
KeywordsLinguisticsMorphemeLanguage familyMorphology (biology)Austronesian languagesContext (archaeology)Computer scienceHistoryPhilosophy

Abstract

fetched live from OpenAlex

Yogad is an Austronesian language spoken in the Northern Philippines by approximately 16,000 speakers.Previous descriptive work on Yogad follows a nonaprioristic approach, where no categories are imported from other languages.This description makes it difficult to gloss functional morphemes or to discuss Yogad in a cross-linguistic context.In this paper, I reanalyze the morphology of Yogad following a restrictivist approach to language description, particularly the verbal affixes, case marking particles, and personal pronouns.I provide a description of Yogad morphology which does not exoticize the language, is informed by theory, and can contribute to discussions and debate within the language family.I situate Yogad in the larger context of Austronesian languages through a comparative study and a diachronic discussion.I provide two descriptions of Yogad morphology, following the analyses and theories held by the two sides of the Austronesian voice debate.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.007
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.060
GPT teacher head0.321
Teacher spread0.261 · 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
Published2022
Admission routes1
Has abstractyes

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