Comparing the effect of skewed and balanced input on English as a foreign language learners’ comprehension of the double-object dative construction
Bibliographic record
Abstract
ABSTRACT According to usage-based approaches to acquisition, the detection of a construction may be facilitated when input contains numerous exemplars with a shared lexical item, which is referred to as skewed input. First language studies have shown that skewed input is more beneficial for the acquisition of novel constructions than balanced input, in which a small set of lexical verbs occurs an equal number of times. However, a second language (L2) study of datives found no advantage for skewed input compared to balanced input. The present study compared the effectiveness of skewed and balanced input at facilitating the comprehension of the double-object dative construction in L2 English. Over a 2-week period, Thai English as foreign language learners (N = 78) completed comprehension tests and a treatment activity that provided either skewed first, skewed random, or balanced input. The results indicated that balanced input was most effective at promoting comprehension of double-object datives. The implications are discussed in terms of the benefits of different types of input for L2 learners.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".