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Record W2986398901 · doi:10.3389/fcomm.2019.00054

The Morphophonology of Intraword Codeswitching: Representation and Processing

2019· article· en· W2986398901 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Communication · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of Victoria
FundersNational Science Foundation
KeywordsPhenomenonAffixLinguisticsComputer scienceLexiconFeature (linguistics)Natural language processingArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

This paper serves as a critical discussion of the phenomenon of intraword code-switching (ICS), or the combining of elements (e.g. a root and an affix) from different languages within a single word. Extensive research over the last four decades (Poplack, 1988; MacSwan, 2014; Myers-Scotton, 2000) has revealed CS to be a rule-governed speech practice. While interword CS is widely attested, intraword code-switching has been argued to be impossible (Bandi-Rao & DenDikken, 2014; MacSwan & Colina, 2014; Poplack, 1980).However, ICS has recently been documented in language pairs ranging from English/Norwegian (Alexiadou, Lohndal, Afarli & Grimstad, 2015) to Nahuatl/Spanish (MacSwan, 1999) to Greek/German (Alexiadou, 2017), and is a robust phenomenon.We review the foundational research on ICS, followed by an examination of the phenomenon from the perspectives of knowledge and skill. First, we examine intraword CS as part of a bilingual’s I-language to determine the morphological and phonological restrictions on the phenomenon. We operationalize these restrictions within a Distributed Morphology (DM) framework (e.g., Halle & Marantz, 1994) in which the traditional lexicon is split into three lists. List 1 contains lexical roots and grammatical features or feature bundles, while Lists 2 and 3 detail instructions for phonological realization (i.e., rules for Vocabulary Insertion) and semantic interpretation, respectively. Here we probe the question of whether words which have morphological mixing also have phonological mixing. Second, building on the DM machinery, we present an account for intraword CS in performance via the modular cognitive performance framework of MOGUL (Truscott & Sharwood Smith, 2014). This modular architecture assumes a) that lexical items are constituted by chains of representations and b) that extra-linguistic cognitive mechanisms (e.g. goals, executive control) play a role in ICS (Green & Abutalebi, 2013). ICS is licensed by a bilingual mode of communication (following Grosjean, 1998) where the act of CS itself serves an illocutionary goal; it is the real-world context which triggers the complex CS system. Thus, viewing intraword CS as an I-language and an E-language phenomenon provides an explanatory model of the dynamic knowing that and knowing how which is manifest in the phenomenon of ICS.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.140

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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