The Morphophonology of Intraword Codeswitching: Representation and Processing
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".