Mothers’ Measured Reading and Their Preschool Children’s Language and Reading Proficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study investigated 1) whether mothers' measured reading has predictive power for their preschool children's language and reading over and above SES predictors, and 2) whether mothers' measured reading can help to compensate for negative influences on children's language and reading that arise from SES factors such as speaking a minority language and belonging to some ethnic minorities. One hundred fifty-eight children (aged 3-5 years) and 156 mothers of low income and low educational background from a large city in Western Canada participated. Mothers were interviewed regarding demographic information and their reading was measured. Children were administered language and reading tests. The findings support the conclusion that mothers' measured reading predicts their children's language and reading prior to schooling, (b = .63, p .05, on TERA-2; b = 4.04, p .001, on PPVT-III; coefficients are unstandardized) and that mothers' education and mothers' reading proficiency do not serve as proxies for each other. Our results not only confirm the predictive power of mothers' education on their children's language and reading already shown in the literature, but also provide evidence of the predictive power of mothers' measured reading level over and above their education level. Our results also point to the crucial importance of finishing high school with commensurate reading attainment. Benefits from high school completion accrue not only to the adults but also to their children. Intervention programs must shift focus to improvements to mothers' reading as their primary goal in addition to their children's language and reading.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it