A Longitudinal Study on the L2 Word Depth Knowledge (Meaning and Collocation) Development in China from Dynamic Systems Theory Perspective
Bibliographic record
Abstract
This study, adopting a longitudinal approach, traces the dynamic word knowledge development of two essential dimensions (meaning and collocation) both receptively and productively in Chinese context. Seven freshmen students (from the same class in a university in Guangzhou) of different language levels were selected to be observed of their English word knowledge development over one year’s time. The results suggest that word knowledge develops with slowdowns and recessions and the dimensions of knowledge develop in an unbalanced way, an indication of the dynamic and complex nature of language learning. Receptive knowledge shows a much more active development than productive knowledge as it grows faster but with more losses as well. Collocation though always shows poorer acquisition results in the two tests, demonstrates a faster growth than meaning knowledge. As to the individual differences, participants who were tested as the higher level of learners did not always show better development in word depth knowledge during the year. The results also reveal the participants’ learning preferences (e.g. passive learning style and active learning style). This study sheds light on the L2 vocabulary learning and teaching in Chinese context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".