Cross-linguistic influence in third language acquisition : psychological perspectives
Bibliographic record
Abstract
The effect of linguistic distance, L2 status and age on crosslinguistic influence in L3 acquisition, Jasone Cenoz (University of the Basque Country) roles of L1 and L2 in L3 production and acquisition, Bj rn Hammarberg (Stockholm University) interlanguage transfer and competing linguistic systems in the multilingual mind, Gessica De Angelis & Larry Selinker (University of London) lexical transfer in L3 production, H kan Ringbom ( bo Akademi University, Finland) activation or inhibition? the interaction of L1, L2 and L3 on the language mode continuum, Jean-Marc Dewaele (University of London) lexical retrieval in a third language, Peter Ecke (University of Arizona) plurilingual lexical organization, Anne Herwig (University of Dublin) learners of German as an L3 and their production of German prepositional verbs, Martha Gibson, Britta Hufeisen (The Technical University of Darmstadt) & Gary Libben (University of Alberta) too close for comfort? sociolinguistic transfer from Japanese into Korean as an L3, Robert F. Fouser (Kumamoto Gakuen University, Japan) New Uses for Old Language - crosslinguistic and crossgestural influence in the narratives of non-native speakers, Eric Kellerman (University of Nijmegen, Holland).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".