Examining the effects of crosslinguistic awareness on the acquisition of English possessive determiners: the case of Brazilian Portuguese speakers
Bibliographic record
Abstract
Possessive determiners (PDs) his and her are challenging for L2 learners to acquire, and this difficulty has been attributed to several factors, including negative L1 transfer effects (White et al., 2007 White, J., Muñoz, C., & Collins, L. (2007). The his/her challenge: Making progress in a ‘regular’ L2 programme. Language Awareness, 16(4), 278–299.[Taylor & Francis Online] , [Google Scholar]). What researchers have not yet considered is how PDs are acquired by learners whose L1 predicts positive transfer effects. To address this question, the present study investigated the PD interlanguage of Brazilian Portuguese (BP) speakers, whose L1 has a PD system that is similar to English. It also considered the effects of crosslinguistic awareness on performance of this linguistic feature, as awareness between and across languages has been shown to support positive transfer (e.g., Gibson & Hufeisen, 2008 Gibson, M., & Hufeisen, B. (2008). Metalinguistic processing control mechanisms in multilingual learners of English. International Journal of Multilingualism, 3(2), 139–153.[Taylor & Francis Online] , [Google Scholar]). Two written and two oral tasks were used to measure performance, and a stimulated recall task was used to measure awareness. The results showed that BP-speakers exhibited advantages in their acquisition of his and her in comparison to previously studied L1 groups, and that learners who verbalised awareness of a crosslinguistic PD rule outperformed those who did not on two of three PD tasks. These findings contribute to the research suggesting that building crosslinguistic awareness of L1/L2 similarities could be an effective approach for supporting language learning.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".