Re-examining the Influence of Native Language and Culture on L2 Learning: A Multidisciplinary Perspective
Bibliographic record
Abstract
The role of the native language (NL) and culture in a second language (L 2 ) context has been debated for over 200 years (Gass, 1996). Most of the early debate, however, did not concern learning per se, but was centered around the value of using the NL in the classroom. The issues and questions surrounding the use of NL information have changed. Within the past 50 years we have witnessed great flux in research directions, traditions, and assumptions. On the other hand, many institutions in the Arab world have prohibited the use of NL in the classroom, which is commonly perceived to be an impediment to L 2 learning. This pedagogical decision, however, is not fully supported by recent research findings. Accordingly, the present study, first, traces the conceptual history of the notion “language transfer” from its early beginning to its current position within Universal Grammar. Second, it problematises the exclusion of L 1 from the classroom and supports the notion of incorporating students’ input into pedagogical decision making processes. Third, it shows, as an example, how L 1 culture affects the written production of L 2 learners.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".