A Longitudinal Study of Kindergarten Children At Risk for Reading Disabilities
Bibliographic record
Abstract
Over the past decade, educators and researchers concerned about children with reading disabilities have called for widespread adoption of early identification tools and early effective programming. This call may be the result of, in part, what Stanovich calls "Matthew effects in reading." That is, when stakeholders delay identification and support for young children struggling to read, the variance of individual differences in reading will inevitably increase, creating a widening of the gap between strong and struggling readers. In this longitudinal study, reading achievement data from 382 children were collected as they progressed from kindergarten through Grade 3. In kindergarten, children were screened with a battery of phonological awareness measures. Percentile rank scores were collected, and children were identified as having poor, average, or strong phonological awareness. As children moved through Grades 1, 2, and 3, reading-based data were collected in the spring of each year. Results indicated that, in general, as children progressed from kindergarten to Grade 3, those in lower ranks of reading achievement were likely to remain in the lower ranks, and furthermore, at each progressing data collection point struggling readers fell further behind their grade-level reading peers. In other words, as each year passed the variance between strong and struggling readers increased significantly. The authors hypothesized that this finding is consistent with the "Matthew effect"-the rich were getting richer while the poor were getting poorer.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".