Bibliographic record
Abstract
This book deals with the effects of three different learning contexts mainly on adult, but also on adolescent, learners’ language acquisition. The three contexts brought together in the monograph include i) a conventional instructed second language acquisition (ISLA) environment, in which learners receive formal instruction in English as a Foreign Language (EFL); ii) a Study Abroad (SA) context, which learners experience during mobility programmes, when the target language is no longer a foreign but a second language learnt in a naturalistic context; iii) the immersion classroom, also known as an integrated content and language (ICL) setting, in which learners are taught content subjects through the medium of the target language—more often than not English, used as the Lingua Franca (ELF). The volume examines how these contexts change language learners’ linguistic performance, and also non-linguistic, that is, it throws light on how motivation, sense of identity, interculturality, international ethos, and affective factors develop. To our knowledge, no publication exists which places the three contexts on focus in this monograph along a continuum, as suggested in Pérez-Vidal (2011, 2014), with SA as ‘the most naturalistic’ context on one extreme, ISLA on the other, and ICL somewhere in between, while framing them all as international classrooms. Concerning target languages, the nine chapters included in the volume analyze English, and one chapter deals with Spanish, as the target language. As for target countries in SA programmes, data include England, Ireland, France, Germany, and Spain in Europe, but also Canada, China, and Australia. While the main bulk of the chapters deal with tertiary level language learners, a language learning population which has received less attention by research thus far, one chapter deals with adolescent learners. Carmen Pérez-Vidal, Sonia López, Jennifer Ament and Dakota Thomas-Wilhelm all served on the organizing committee for the EUROSLA workshop held at the Universitat Pompeu Fabra, Barcelona, in May 2016. It is from this workshop that this monograph was inspired
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.070 | 0.007 |
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".