Bibliographic record
Abstract
This honours project examines the use of BA and BEI constructions for school-aged children. The study includes in-person interviews and conversation sessions to demonstrate how syntactic priming is effective for passive BEI constructions. It emphasizes the combination of priming and translation activities of the research design. Research results summarize that girls use more BA constructions than boys, and younger children make verb omissions and word-order errors. This study provides unique insights into bilinguals ‘syntactic productivity in BA and BEI constructions and aligns with second language acquisition (SLA) and Speech-Language Pathology (SLP). Language educators can focus on the enjoyment element in their language teaching methods for elementary students whose second language is English. Future studies can find the patterns in the syntactic performances of BA and BEI constructions among those with and without language impairments (LI). Keywords: Bilingualism, Age of Acquisition (AoA), Heritage Language (HL) Learning, BA and BEI Construction, Syntactic Priming, Interlanguage (IL), Chinese Heritage Language (CHL) learner
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 | 0.002 |
| 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 | 0.000 |
| 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".