Leveraging Listening Texts in Vocabulary Acquisition for Low-literate Learners
Bibliographic record
Abstract
To date, the vast majority of research in second language (L2) vocabulary acquisition has looked at reading, but relatively few studies have explored the potential for vocabulary acquisition through listening. As for participants involved, studies concerning first language (L1) acquisition have mainly focused on pre- and emergent-reading children, whereas those concerning L2 acquisition comprised learners already highly literate in their L1. Like other research areas of second language acquisition (SLA), learners with low or no literacy in their L1 have been virtually neglected in these studies. Clearly, who we study determines what we know in SLA, yet there exists a significant gap in research literature regarding how understudied, low-literate (and illiterate) populations with strong oral traditions may acquire L2 vocabulary through listening. This paper attempts to bridge the gap in research on cognitive processing and L2 vocabulary acquisition through listening. In light of this, relevant pedagogical implications for low-literate populations are discussed. Jusqu’à présent, l’immense majorité de la recherche sur l’acquisition du vocabulaire de la langue seconde (L2) s’est concentrée sur la lecture, mais très peu d’études ont exploré le potentiel de l’acquisition du vocabulaire par l’écoute. En ce qui concerne les participants impliqués, les études sur l’acquisition de la première langue (L1) se sont principalement concentrées sur des enfants au stade de pré-lecture ou d’apprentissage de la lecture, alors que celles traitant de l’acquisition de la L2 incluaient des apprenants qui avaient déjà un haut niveau de littératie dans leur L1. Comme dans d’autres domaines de recherche sur l’acquisition de la langue seconde (ALS), les apprenants dont le niveau de littératie est bas ou inexistant dans leur L1 n’ont presque pas fait l’objet de ces études. Il est clair que les personnes que nous étudions déterminent ce que nous savons en matière d’ASL, cependant il existe dans la documentation de recherche un vide significatif concernant la capacité des populations sous scolarisées à faible niveau de littératie (et illétrées) dont les traditions orales sont fortes, à acquérir le vocabulaire de L2 par l’écoute. Cet article essaie de combler le vide dans la recherche sur le processus cognitif et l’acquisition du vocabulaire de la L2 par l’écoute. Sous cet angle, nous discutons des implications pédagogiques pertinentes pour les populations à faible niveau de littératie.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".