Moderate Gaming and Internet Use Show Positive Association with Online Reading of 10-Year-Olds in Europe
Bibliographic record
Abstract
The paper analyses how four screen activities relate to reading scores using the representative sample of 21,217 ten-year-olds who sat online and offline Progress in International Reading Literacy Study (PIRLS) test in six high-income European countries. In regression models, gaming and Internet use showed a right-skewed inverted U-shape relationship to online reading with moderate use (30–60 min daily) showing a positive association when compared to both no-use and heavy use (above 2 h). Online chatting and watching videos showed negative relationship to online reading above the threshold of approximately one hour daily. Quantile regression showed that all four types of screen time had similar influence on top and bottom performers except for gaming over 2 h daily which was associated with 26-point (or over a quarter of a standard deviation) lower score for low-performers and 6-point lower score for top-performers. The paper documents the emergence of online-offline reading gaps: children who reported no screen use scored 6–11 points lower on online than offline test. Similarly, children who spent more time online scored higher on online tests than on offline tests. Whenever the heavy screen use yielded significant results, it was associated with higher online score (ranging from 8 to 16 points) when compared to offline score. A common finding for all screen activities, testing modes and groups of performers is an adverse effect on reading of more than two hours daily of screen time.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".