THE EFFECT OF EXPERIENCE ON ADULTS' ACQUISITION OF A SECONDLANGUAGE
Bibliographic record
Abstract
Previous research has suggested that child but not adult immigrants to the United States and Canada make regular progress learning English as their length of residence (LOR) increases. If children and adults received the same kind of second language (L2) input, such evidence would support the existence of a critical period for L2 acquisition. The present study compared groups of Chinese adults living in the United States who differed in LOR in order to assess the role of input in adults' naturalistic acquisition of an L2. We assessed the Chinese participants' identification of word-final English consonants (experiment 1), their scores on a 144-item grammaticality judgment test (experiment 2), and their scores on a 45-item listening comprehension test (experiment 3). The Chinese participants were assigned to one of four groups ( n = 15 each) based on LOR in the United States and their primary occupation (students vs. nonstudents). Significantly higher scores were obtained for the students with relatively long LORs than for the students with relatively short LORs in all three experiments. However, the difference between the nonstudents differing in LOR was nonsignificant in each instance. The results suggested that the lack of an effect of LOR in some previous studies may have been due to sampling error. It appears that adults' performance in an L2 will improve measurably over time, but only if they receive a substantial amount of native speaker input.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".