Bibliographic record
Abstract
This study examined the degree to which the age that a child learns to read affects his or her future academic success. In recent years, society and school districts, as well as an increasing number of parents, have been urging children to read at earlier ages. Therefore, in order to investigate the research question, an extensive survey was developed and electronically dispersed to individuals eighteen years of age or older. The survey was completed by 220 respondents, collected, and evaluated. The survey results provided quantitative data on respondents’ demographic backgrounds as well as their childhood reading and academic histories. A significant number of respondents, 85%, said they regarded the age a child learns to read as important. Over 82% of early readers, ages three and four, described their overall academic success as either “Excellent” or “Very Good”, which was 12% higher than the respondents who did not learn to read until age six or older. The factors that were not impacted by the age the respondents learned to read was whether or not they went to college or earned advanced degrees. Therefore, while academic success is often perceived as getting good grades in school, is that really enough? The respondents who learned to read at an early age generally earned good grades in school, but that did not necessarily translate to being more likely to go to college or earn an advanced degree, which is a strong measure of overall academic success.
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.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".