Written narrations by 8‐ to 10‐year‐old Turkish pupils in Flemish primary education: A follow‐up of seven text features
Bibliographic record
Abstract
This semi‐longitudinal study examined the development of narrative writing quality of young Turkish second language learners in mainstream Dutch‐only education, and the impact of student‐level and classroom‐level predictors of narrative writing quality, using hierarchical linear modelling. Writing samples of 106 third graders and 111 fourth graders of seven Flemish primary schools were collected at the beginning and at the end of the school year. Measures included one holistic primary trait judgement, and six objective indices of text quality. Student‐level predictors included age, SES and home language, while the classroom‐level predictor focused on the home language pattern of the classroom. There was a significant mean growth for each index in each grade, but effect sizes differed from quite large for content and word level indices over moderate for sentence level indices to small for the text level index. Home language (Turkish) had a significant negative effect on all but one variables, particularly in Grade 4, while the negative effect of low SES was much more limited. A supplementary negative effect was found for homogeneity of classroom population. Implications of the study highlight the importance of student and classroom characteristics in writing achievement as well as the need to consider the poor performance of Turkish children.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".