Acquisition during normative code-mixing: Trinidadian children’s varilingual pronoun usage
Bibliographic record
Abstract
While monolingual English speakers acquire most pronouns by age 5, acquisition amid prevalent, normative code-mixing, such as in Trinidad, is underexplored. This study examines how Trinidadian 3- to 5-year-olds express third-person subject, object, reflexive and possessive pronouns and factors influencing pronoun choices. Seventy-five preschoolers produced pronouns via a word elicitation task conducted in Trinidadian English Creole and Trinidad and Tobago English. Responses were coded for children’s age, gender, district and socioeconomic status; task language; grammatical gender/number; and response form. Conditional inference trees facilitated statistical analysis. When choices were available, children exhibited variable production, with Creole forms often dominant. Grammatical gender influenced whether English or Creole pronouns were selected. Task language influenced possessive pronoun choices, indicating developing sociolinguistic competence. The non-significance of other variables suggests widespread mixing of English and Creole pronouns. Findings underscore the importance of describing understudied populations, especially where variation is inherent, to ensure accurate language assessment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".