From Storybooks to Novels: A Retrospective Approach Linking Print Exposure in Childhood to Adolescence
Bibliographic record
Abstract
Despite the far-reaching advantages associated with leisure reading, it is an activity that fewer adolescents are choosing to pursue. The present study used a retrospective correlational approach to investigate shared storybook reading in childhood and current print exposure in 45 parent-adolescent dyads. Parents and adolescents completed a Retrospective Title Recognition Test, identifying storybook titles from a backdated list (books published before 2007) containing both real titles and foils. Adolescents also completed Activity Preference and Reading Enjoyment/Frequency questionnaires to assess reading habits as well as an Author Recognition Test to assess current print exposure. In addition, they were asked to name their favorite childhood storybook and favorite current author to investigate whether these two abilities were linked to print exposure. Vocabulary, reading, and spelling skills were also measured. A hierarchical multiple regression demonstrated that adolescents' Retrospective Title Recognition Test scores accounted for unique variance in their Author Recognition Test scores, above and beyond literacy skills. Mediational analyses demonstrated that print exposure contributed to word reading and spelling scores. Our findings highlight the impact of parents' shared storybook reading with children. Here, early reading experiences related to later reading preferences, which in turn, were associated with literacy skills in adolescence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".