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Record W2951778018

Using questionnaires as a tool for comparative linguistic field research: Two case studies on Javanese

2019· book-chapter· en· W2951778018 on OpenAlexfundno aff
Jozina Vander Klok, Thomas J. Conners

Bibliographic record

VenueScholarSpace (University of Hawaii at Manoa) · 2019
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaUniversité Paris Diderot
KeywordsLinguisticsField (mathematics)SociologyNatural language processingPsychologyComputer sciencePhilosophyMathematics
DOInot available

Abstract

fetched live from OpenAlex

In this paper, we discuss how written questionnaires for targeted constructions can be a beneficial tool for comparative linguistic field research through two case studies on Javanese (Austronesian; Indonesia). The first case study is based on a questionnaire designed to elicit how a language or a dialect expresses the semantic meaning of modality (Vander Klok 2014); we show how it can be implemented in three different ways for comparative linguistic field research. The second case study is based on a questionnaire which investigates the morphosyntax of polar questions across four Javanese dialects; we show how items can be designed to maximize direct comparison of features while still allowing for possible lexical, phonological, or morphosyntactic variation. Based on these two studies, we also address methodological challenges that arise in using questionnaires in comparative linguistic field research and offer best practices to overcome these challenges.\nhttp://hdl.handle.net/10125/24858

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.869
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.348
GPT teacher head0.427
Teacher spread0.078 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations4
Published2019
Admission routes1
Has abstractyes

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