The Effects of L2 Reading Skills on L1 Reading Skills through Transfer
Bibliographic record
Abstract
This study investigated whether transfer from L2 to L1 in reading occurs, and if so, which reading sub-skills are transferred into L1 reading. The aim is to identify the role of second language reading skills in L1 reading skills by means of transfer. In addition, the positive effects of the second language transfer to the first language in the context of reading skills and sub-skills were analyzed. Fifty-three native Turkish-speaking adults English language learners were tested in this study. These participants were university students who had the same L1 Turkish proficiency backgrounds. While 26 students took L2 reading courses for four months, the other 27 students did not take any L2 reading courses. After four months of L2 reading courses, these two groups were given a standard L1 (Turkish) reading test. The Turkish reading test included vocabulary, comprehension, grammar and reading sub-skills questions. The results revealed that L1 reading skills were affected positively by the L2 reading skill transfer. The study reveals which L1 reading sub-skills are more developed by L2 reading skills transfer. For further studies, the correlations in L1 and L2 courses may open a way in language curriculum design. Both courses can be designed as an adjunct course formulated on the skill-based syllabus model, and reading skills can be transferred cross-linguistically. Thus, L2 reading proficiency will be transferred to L1 proficiency.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".