Loanwords and Code-switching: Distinguishing Between Language Contact Phenomena in Ch'ol
Bibliographic record
Abstract
Language contact phenomena, including loanwords and code-switch- ing, have been the subject of numerous recent studies in sociolinguistics.Loanwords are words borrowed from one language for use in another, while code-switching occurs when speakers mix lexical or grammatical elements from two or more languages in a single speech act.Although numerous studies extensively examine both linguistic phenomena, the boundary between them remains unclear, leaving one to question how to distinguish loanwords from code-switching.Using data from Ch'ol, a Mayan language spoken by more than 100,000 individuals in southeast- ern Mexico, this paper clearly identifies the characteristics of loanwords and code-switching and outlines the differences between them.This paper argues that on the most basic level, one can distinguish loanwords and code-switching on the basis of assimilation: loanwords demonstrate phonological, morphophonemic, or lexical integration, while code- switched words and phrases do not.Secondary criteria for identifying loanwords include the use of words by speakers of varying levels of bilingualism as well as the absence of native equivalents for specific words.Code-switching is practiced only by bilingual Ch'ols.Through its analysis, this paper argues that code-switching may be indicative of emerging changes in a speaker's cultural and linguistic identity.
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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 | 0.001 | 0.017 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".