Language of Early Reading Instruction: A Correlate of Print Exposure
Bibliographic record
Abstract
The Matthew effects suggest that children who struggle when learning to read are less likely to read for pleasure later in life compared to children who ease into reading quickly. One aspect of early literacy instruction that might hamper reading progress is learning to read simultaneously in two languages. Despite the long-lasting and widespread benefits of bilingualism, early setbacks in reading development might carry lasting effects for later reading habits. We investigated whether present-day print exposure of adults who learned to read in their first language were different from those who learned to read in two languages. Adults completed: Bilingual Author Recognition, Viewing Recognition, and English and French Word Recognition Tests. Participants who reported that reading instruction took place in their first language recognized more authors than those who learned to read in two languages. These first-language learners were also better at identifying real English words. Bilingual learners were superior at identifying real French words on the corresponding task. Lastly, both groups demonstrated similar viewing habits. The findings from this retrospective study align with the Matthew effects and suggest that a focus on first language reading instruction in Grades 1 and 2 remains correlated with print exposure 25 years later.
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".