Cree-English intrasentential code-switching: Testing the morphosyntactic constraints of the Matrix Language Frame model
Bibliographic record
Abstract
Abstract This study examines the morphosyntactic constraints on Cree-English intrasentential codeswitching involving mixed nominal expressions to test the Matrix Language Frame (MLF) model. The MLF model is one of the most influential frameworks in the field of contact linguistics used in the study of grammatical aspects of codeswitching and other contact-induced phenomena. The three principles associated with MLF, viz., the Matrix Language Principle, the Asymmetry Principle and the Uniform Structure Principle, were tested on data consisting of 10 video recordings (constituting of 323 tokens of English nouns in mixed utterances) collected from the speech of a Cree child, aged 04;06 - 06;00. The data is drawn from Pile’s (2018) thesis which is based on the data collected from the Chisasibi Child Language Acquisition Study (CCLAS). The results of the analyses suggest general support for the three principles since, in the entire data set, not a single counter example has been recorded. The Cree-English bilingual data appears asymmetrical in structure, where the Matrix Language, namely Cree, provides morpheme order and outsider late system morphemes, and consequently, is responsible for the well-formedness and morphosyntactic frame of bilingual clauses..
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".