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Codeswitching in words and phrases

2025· article· en· W4413609870 on OpenAlexaff
John Archibald

Bibliographic record

VenueContinua. · 2025
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLinguisticsWord (group theory)Computer sciencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

While the properties of bilingual codeswitching are well-documented, and switching morphology within words is often addressed, the properties of intraword phonology are less-understood. Morphemes from more than one language occur within a single word (e.g., a root from one language and affixes from the other). Furthermore, the affixes come only from the language which generates the syntactic tree while the root can be taken from either language. The data also strongly suggest that within such a morphologically mixed word, the phonology does not switch, a property I refer to as phonological uniformity. The key question explored in this paper is: why is phonological mixing in a morphologically mixed word not allowed? I first present evidence that there is phonological activation of both languages even in a monolingual task such as a Lexical Decision Task or silent reading which is consistent with an integrated I-phonological grammar. I provide a reanalysis of some fascinating data from Delgado et al. (2022) in arguing against a phase-based account of phonological uniformity. The mechanism which ensures that the phonology of the X0 matches the language of the affixes is Match Theory (Selkirk, 2011). The preferential mapping is between (a) syntactic phrases (XPs) and phonological phrases (f), and (b) syntactic heads (X0s) and prosodic words (w). Match Theory’s (monolingual) assumption that syntactic and phonological structure are isomorphic can easily be extended to bilinguals through language tags (Green & Abutalebi, 2013). I recast the Match Theory machinery within the framework of Co-phonologies (Sande, Jenks & Inkelas, 2020). In order to account for the differential behaviour of determiners in codeswitched simple DPs (e.g. the mesa) versus codeswitched complex DPs (e.g. the brown mesa) I show how a combination of the notion of the head of the phonological phrase, the free (as opposed to affixal) clitic status of the English determiner, and the parsing of an English vocabulary item via the Spanish contrastive hierarchy explains the phonological properties observed. A Null Theory phonological account of phonological uniformity is argued to be preferred over a phase-based account.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.682
Threshold uncertainty score0.139

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 head0.005
GPT teacher head0.255
Teacher spread0.250 · 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 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
Published2025
Admission routes1
Has abstractyes

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