The L1 Semantic Retrieval of L2 Words: Evidence from Advanced L2 Learners’ Reaction Times
Bibliographic record
Abstract
According to the first language (L1) lemma mediation hypothesis, second language learners, regardless of their level of second language (L2) proficiency, access the meaning of L2 words via their first language (Jiang, 2004). To test this hypothesis, a semantic judgment task was conducted on 30 advanced Arab speakers of English, in which they were presented with 86 pairs of English words and had to decide whether each pair was semantically related. Some semantically related pairs are classified as same translation pairs because their members share the same L1 translation, whereas others are semantically related but do not share the same L1 translation, hence they are classified as different translation pairs. Two instruments were used to record the reaction times and determine accuracy: DMDX and Gorilla. The results revealed that the highly proficient L2 speakers rated same translation pairs as semantically related significantly faster than their responses to different translation pairs. When compared with the 28 native speakers’ results, there was a significant difference in the reaction times of the two groups. This provides evidence that the underlying processes of L1 and L2 vocabulary acquisition is substantially different: L2 learners rely on their well-established conceptual system to access the meaning of L2 words.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".