Multilingualism and third language acquisition
Bibliographic record
Abstract
The purpose of this book is to present recent studies in the field of multilingualism and L3, bringing together contributions from an international group of specialists from Austria, Canada, Germany, Portugal, Spain, Switzerland, Turkey, and United States. The main focuses of the articles are three: language acquisition, language learning and teaching. A collection of theoretical and empirical articles from scholars of multilingualism and language acquisition makes the book a significant resource as the papers present a wide perspective from main theories to current issues, reflecting new trends in the field. The authors focus on the heterogeneity and complexity that characterize third language acquisition, multilingual learning and teaching. As the issues addressed in this book intersect, it represents an asset and therefore the texts will be of great relevance for the scientific community. Part I presents different topics of L3 acquisition, such as syntax, phonology, working memory and selective attention, and lexicon. Part II comprises texts that show how the research on language acquisition informs pedagogical issues. For instance, the role of the knowledge of previous languages in the teaching of L3, the attitudes of multilingual teachers to plurilingual approaches, and the benefits of crosslinguistic pedagogy versus classroom monolingual bias. In sequence, Part III consists of texts on individual learning strategies, such as motivation and attitudes, crosslinguistic awareness, and students’ perceptions about teachers’ “plurilingual nonnativism”.
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 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".