Sak pase (what's going on)? : reading and spelling skills of bilingual Haitian children in French Canada
Bibliographic record
Abstract
Linguists and psychologists alike have long overlooked the study of creole languages. We know very little about language and reading acquisition in young creole speakers. The aim of the present study was to examine the development of reading-related skills in native speakers of Haitian Creole (HC), a French-based creole, educated in French. In order to isolate the effects of speaking two highly similar languages, we compared Haitian children in 1st and 2nd grade to Spanish-French bilingual children and French monolingual children from European descent. Children from our sample were from five different schools in Montreal and had similar socioeconomic status. Participants were tested individually over three sessions on French standardized and experimental tasks assessing metalinguistic awareness, reading, comprehension, vocabulary and mathematical skills. Bilingual children were also tested on reading and spelling tasks in HC and Spanish. Results showed that HC and Spanish bilinguals performed as well as French native speakers on metalinguistic and reading tasks. However, Spanish-speaking children received lower scores than children in the two other groups on a receptive vocabulary measure. In an experimental task comparing the spelling of words of varying phonological similarity in HC and French, Haitian children had more difficulty spelling words that are cognates in HC and French than homophones or noncognate translations. Findings from this study were interpreted in light of the Bilingual Interactive Activation model (Dijsktra & Van Heuven, 1998).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".