The Second Language Acquisition of Past Tense Marker in English by L1 Speakers of Chinese LE PASSÉ DANS L'ACQUISITION DE L'ANGLAIS EN TANT QU'UNE DEUXIÈME LANGUE PAR LES LOCUTEUR DU CHINOIS
Bibliographic record
Abstract
This study is considered the L2 acquisition and underlying of past tense marker, focusing on whether or not L2 learners of English are successful in associating the grammatical properties with Chinese language. Although the dataset is small, the results showed that Chinese speakers are able to acquire the past-tense marker although Chinese language has none of this feature. The L1 Chinese speakers are able to acquire the regular past-tense marker better co mpared to the irregular form. Keywords: Second language acquisition; Past tense marker; L1 Chinese speakers; Irregular form Resume: Cet article etudie l'acquisition d'une deuxieme langue, et en particulier l'apprentissage du passe, en se concentrant sur le fait si les apprenants de l'anglais pouvaient reussir a associer les proprietes grammaticales de la langue anglaise avec la langue chinoise. Bien que l'ensemble des donnees est faible, les resultats montrent que les locuteurs du chinois sont capable de maitriser le passe, meme si la langue chinoise n'a pas cette fonctionnalite. Les locuteurs du chinois maitrise mieux le passe en forme reguliere par rapport en forme irreguliere.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".