Different Measures of Print Exposure Predict Different Aspects of Vocabulary
Bibliographic record
Abstract
Abstract The authors examined whether different measures of print exposure assess the same underlying concept and how these different measures relate to vocabulary breadth and depth. One hundred forty‐seven students attending the third year of kindergarten in Jining, China, were assessed on nonverbal IQ , vocabulary breadth and depth, and their knowledge of book titles. Parents also participated in the study by filling out a questionnaire on the frequency of shared book reading and the number of children's books at home, recording their daily parent–child reading activities (diary), and completing the children's title and author recognition checklists. Results of exploratory factor analysis indicated that the measures of print exposure were loading on three interrelated factors. The items measuring the frequency of shared book reading at home along with diary formed one factor, the children's title recognition checklist and number of children's books at home formed a second factor, and children's knowledge of book titles formed a third factor. In addition, the results of hierarchical regression analyses indicated that whereas all factors accounted for unique variance (3–6%) in vocabulary breadth, only children's knowledge of book titles predicted vocabulary depth, after controlling for children's age, parents’ education and income, and children's nonverbal IQ . Taken together, these findings suggest that different measures of print exposure may capture different aspects of print exposure and that these aspects may exert a different role in vocabulary breadth and depth.
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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 | 0.006 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".