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Record W3047964325 · doi:10.33137/twpl.v42i1.33527

Variation in subject doubling in Homeland and Heritage Faetar

2020· article· en· W3047964325 on OpenAlexafffundvenueabout
Katharina Pabst, Lex Konnelly, Fiona Wilson, Savannah Meslin, Naomi Nagy

Bibliographic record

VenueToronto Working Papers in Linguistics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaUniversity at BuffaloUniversity of Toronto
KeywordsHomelandVariety (cybernetics)Subject (documents)Variation (astronomy)Heritage languageGeographyHistoryPsychologyPolitical scienceComputer scienceArtificial intelligenceLibrary scienceLawPhysics

Abstract

fetched live from OpenAlex

This paper investigates subject doubling in Faetar, an endangered and understudied variety of Francoprovençal. Comparing Homeland speakers (i.e., speakers who were born and raised in Faeto) and Heritage speakers of the language (i.e., speakers who emigrated to Toronto, Canada after age 18, and their children), we find some striking differences. Our results show that subject doubling is grammatically constrained in the source variety: Homeland speakers favor doubling in new information contexts, while Heritage speakers do not. There is also evidence for a change in progress among Homeland speakers, with younger speakers using more subject doubling than older speakers. This change is not mirrored by the Heritage speakers. We propose that this is because the Heritage speakers left the Homeland either before or around the time that the youngest Homeland speakers in our sample were born, resulting in them having missed out on this change. This highlights that both Homeland and Heritage varieties are dynamic and may develop in different directions. Additionally, this study helps complete the picture previously reported for variation between overt (single or doubled) and null subjects in these two varieties: an ongoing decrease in null subject rates in the Homeland variety and stability in the Heritage variety (Nagy et al. 2018).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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 teacher head, 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

Citations2
Published2020
Admission routes4
Has abstractyes

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Same venueToronto Working Papers in LinguisticsSame topicLinguistic Variation and MorphologyFrench-language works237,207