Code-Mixing in the Conversation of Northern Khmer Speakers in Thailand: A Case Study of Teenagers and Middle-Aged Northern Khmer Speakers in Buriram Province
Bibliographic record
Abstract
This study aims to examine the linguistic performance of code-mixing by Northern Khmer (NK) teenagers in Buriram Province while conversing with NK middle-aged speakers in their community. It focuses on types of linguistic units or categories of code-mixing that occur in NK conversation and also on the various situations in which that linguistic unit occurs. It is found that code-mixing between NK and the Thai language occurs on three linguistic levels: morphological, syntactic, and discourse. On the morphological level, 7 categories of Thai words are found: noun, verb, adjective, final particle, quantifier, conjunction, and exclamation word. Three types of code-mixing are found on the syntactic level: Inter-Sentential, Intra-Sentential, and Extra-Sentential Code-Mixing. On the discourse level, code-mixing occurs in the middle and at the end of the NK discourse. There are 6 different situations in NK conversations where these types of code-mixing occur: (1) Greetings (2) Expressing appreciation (3) Expressions of politeness (4) Telling information (General and Specific) (5) Indicative mood, Lexical meaning, and Sentence structure, and (6) English loan words further borrowed from the Thai language. It is also found that NK speakers adopt the morphological processes of reduplication, the sentential structure, and serial verb construction when utilizing the Thai language to mix in their NK word formation and NK sentence structure. Lastly, NK speakers borrow English loanwords from Thai, instead of borrowing them directly from English.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".