Bibliographic record
Abstract
The focus of this special issue is instructed second language acquisition (ISLA). It is to explore some of the most recent developments in this area of SLA research and its implications for classroom instruction. Drawing on some current definitions (Leow, 2015; Loewen, 2015; Nassaji, 2015; Nassaji & Fotos, 2010), ISLA is defined as an area of SLA that investigates not only the effects but also the processes and mechanisms involved in any form-focused intervention (explicit or implicit) with the aim of facilitating language learning and development. Instructed SLA differs from naturalistic SLA, which refers to second language (L2) acquisition taking place through exposure to language in naturalistic language learning settings with no formal intervention (Doughty, 2003). It is also different from classroom instruction with no focus on form. Furthermore, although instructed SLA is often taken to refer to what is learned inside the classroom, instructed SLA can also take place outside the classroom through, for xample, various instructional strategies (such as feedback, tasks, or explanation) that are often associated with instruction. Of course, this does not mean that the processes involved in SLA in and outside the classroom are exactly the same. Although there might be commonalities in learning processes, the classroom context has its unique features that might have an impact on learning. For example, in classroom learning a group of learners come together in a particular place to learn the language jointly during a given period of time. This might have an impact on learning opportunities in terms of the nature of the discourse created, learners’ participation, interaction, and engagement with language. As Allwright (1984, p. 156) pointed out, language interaction in the classroom setting is collectively constructed by all learners and “the importance of interaction in classroom learning is precisely that it entails this joint management of learning.”
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.227 | 0.127 |
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".