Facilitating Lexical Acquisition in Beginner Learners of Italian through Spoken or Sung Lyrics
Bibliographic record
Abstract
The effects of song and elaboration were assessed on the acquisition and retention of lexical items by beginner learners of Italian. Lexical acquisition investigated through an incidental learning experiment based on the premise that growth in L2 vocabulary can be facilitated by subvocal rehearsal and elaborate processing of lexical items. Participants were divided into a control group and four treatment groups. Treatment groups were exposed to a song either in a sung condition or read as a poem, and groups completed lexical tasks designed with low or high levels of elaboration. It was hypothesized that (a) song groups would score higher than poem groups, and that (b) high elaboration groups would score higher than low elaboration groups. With scoring based on Wesche and Paribakht’s Vocabulary Knowledge Scale, at post-test and delayed post-test the song/high elaboration group scored higher than all other groups in both receptive and productive vocabulary learning, suggesting that song and high elaboration are effective in facilitating the acquisition and retention of L2 lexical items and that song and high elaboration activities should be implemented in the L2 curriculum.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".