Learning English as a second language during childhood: A \nlongitudinal case study
Bibliographic record
Abstract
Since the 1950s, hypotheses have been put forth to explain developmental behaviours \nobserved during a learner’s second language (L2) acquisition. Many of these \nhypotheses build on language transfer, which provides a basis for the explanation of \nmany phenomena that learners exhibit during the acquisition of their L2. However, \naspects of transfer have yet to be fully understood; among others, how the critical period \nfor language acquisition affects the relationship between a learner’s first language (L1) \nand L2 has yet to be unfolded. \nTransfer effects and the critical period are indeed potentially confounded when the L2 \nlearner is a child. Furthermore, three questions surrounding transfer still remain. These \nquestions are as follows: What is transferred? What are the conditions for transfer? And \nwhen does transfer occur? Further, while it is commonly observed that children are more \nproficient than adults at language learning, debates still exist as to whether or not a \ncritical period for language acquisition exists at all. In relation to this, there is also the \ndebate concerning whether children introduced to an L2 early in life behave more like a \nfirst or second language learner for that L2. \nThis thesis describes a longitudinal corpus documenting a child named Nura, who is a \nL2 learner of English with Kazakh as her L1 (also with some passive knowledge of \nChinese). More specifically, we focus on Nura’s development of singleton onsets and \nonset clusters which do not occur in her native Kazakh language. The data provides \nevidence for relatively immediate transfer effects through her early acquisition of her \nsingleton onsets and onset clusters. However, the developmental patterns of a number \nof sounds and sound combinations also point to issues in child L2 development in \ncontexts where transfer is not possible, if only for certain phonological dimensions of the \nL2.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".