Individual predictors of first and second language sixth-grade writing performance from kindergarten and grade 1 literacy variables
Bibliographic record
Abstract
This thesis examined the Simple View of Reading (SVR; Hoover & Gough, 1990) in predicting writing performance. Specifically, the present study measured whether unique variance is explained by both decoding and language-related variables in predicting sixth-grade writing performance. The contribution of working memory in predicting writing was also examined, guided by the multi-component model of working memory (Baddeley, 2000). This thesis looked at the intra- and cross-linguistic prediction of these variables in English and French writing from the fall and spring of kindergarten, and the end of grade 1. This thesis does not offer support for the use of the SVR in predicting writing performance, and only provides partial support for the multi-component model of working memory. Results showed that screening for writing performance is best done during the first grade using a task that asks participants to verbally formulate sentences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".