Linguistic factors and invented spelling in children: The case of French beginners in children
Bibliographic record
Abstract
Most studies in the field of first writing experiences in kindergarten have focused on the behaviour of young English-language writers (Treiman & Bourassa, 2000). By considering increasingly acknowledged linguistic factors in spelling development (Seymour, Aro & Erskine, 2003), the present study seeks to contribute to existing studies of young French-language children in Europe by examining the case of young French-Canadian writers (North America). Drawing on 202 kindergarten children, this study seeks to provide a better understanding of the impact of linguistic characteristics on the production of graphemes in an invented spelling task involving the writing of six words. Firstly, it analyzes the “word” effect on the participants’ capacity to produce the appropriate graphemes to represent the phonological information of words (exhaustiveness of the graphemes). Secondly, there is an analysis of unconventional graphemes in order to identify the causes of the deviation from the expected norm. Generally speaking, the findings support the relevance of taking into account the particularities of written French in the spelling development of young French-language children as well as the constructivist view that deviations from the norm are often indicative of difficulties arising from the nature of the writing system to be learned.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".