How does one’s first language writing script modulate second language reading: evidence from the English Reading Online Project (ENRO)
Bibliographic record
Abstract
Abstract Approximately half of the world’s population is multilingual, and many read in a second language. Thus, an open question is whether and how people’s multilingual knowledge impacts their second language reading processes. To this end, we investigated whether competing influences from people’s first language (L1) writing system (i.e., alphabetic, logographic, or alphasyllabic) impact second language (L2) reading of English (alphabetic). Based on models of L1 and L2 reading, we hypothesized that matches/mismatches in people’s L1 and L2 writing scripts would modulate the expected relationship between L2-English reading proficiency and how often people use their L2 in daily life. Using a subsample of 1073 adults from Siegelman et al. (2023), we found that readers with mismatching L1 writing scripts varied on both English Single Word Accuracy and Speed Measures, and English Extended Word Measures, over and above the expected effects of L2 reading usage. L1-alphabetic and alphasyllabic readers were faster and more accurate than L1-logographic speakers on Single Word Speed and Accuracy Measures. L1-logographic readers were also faster but lower in accuracy on Extended Word Measures vs. L1-alphabetic and alphasyllabic readers. These findings indicate that multilingual knowledge and experience mutually constrain L2 reading and suggest future avenues of theoretical and empirical inquiry.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".