Invented spelling in kindergarten as a predictor of reading and spelling in Grade 1: A new pathway to literacy, or just the same road, less known?
Bibliographic record
Abstract
In this study we evaluated whether the sophistication of children's invented spellings in kindergarten was predictive of subsequent reading and spelling in Grade 1, while also considering the influence of well-known precursors. Children in their first year of schooling (mean age = 66 months; N = 171) were assessed on measures of oral vocabulary, alphabetic knowledge, phonological awareness, word reading and invented spelling; approximately 1 year later they were assessed on multiple measures of reading and spelling. Path modeling was pursued to evaluate a hypothesized unique, causal role of invented spelling in subsequent literacy outcomes. Results supported a model in which invented spelling contributed directly to concurrent reading along with alphabetic knowledge and phonological awareness. Longitudinally, invented spelling influenced subsequent reading, along with alphabetic knowledge while mediating the connection between phonological awareness and early reading. Invented spelling also influenced subsequent conventional spelling along with phonological awareness, while mediating the influence of alphabetic knowledge. Invented spelling thus adds explanatory variance to literacy outcomes not entirely captured by well-studied code and language-related skills. (PsycINFO Database Record
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".