Cognitive Approaches to Second Language Acquisition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this chapter, we describe the constructs and working assumptions that characterize such approaches to language learning, with a particular focus on their cognitive underpinnings and how these explain differences between the linguistic forms that distinguish L1 and L2 speakers. We first define constructions as the targets of language learning and then describe the processes of construction learning in terms of exemplar-based, rational, associative learning. Not all constructions are equally learnable by all learners: naturalistic second language learners process open-class words more efficiently than grammatical cues even though the grammatical cues may be more frequent. We outline a usage-based account of this phenomenon in terms of salience, contingency, and redundancy, and explain how effects of learned attention and blocking further limit learning in adult L2 learners. We describe educational interventions taking these findings into consideration and conclude with further readings on usage-based approaches to L2 acquisition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 it