Research Timeline: Form-focused instruction and 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
This article provides a timeline of research on form-focused instruction (FFI). Over the past 40 years, research on the role of instruction has undergone many changes. Much of the early research concentrated on determining whether formal instruction makes any difference in the development of learner language. This question was motivated in part by a theoretical discussion in the field of cognitive psychology over the role of explicit versus implicit learning, on the one hand, and a debate in the field of second language acquisition (SLA) over the role of naturalistic exposure versus formal instruction, on the other. In the early 1980s, for example, based on the notion that the processes involved in second language (L2) learning are similar to those in first language (L1) learning, Krashen (e.g., Krashen 1981, 1982, 1985) made a distinction between learning and acquisition and claimed that an L2 should be acquired through natural exposure not learned through formal instruction. Thus, he claimed that FFI has little beneficial effect on language acquisition. This position, which has also been known as a ‘zero position’ on instruction, was also taken by a number of other researchers who argued that L1 and L2 learning follow similar processes and that what L2 learners need in order to acquire a second language is naturalistic exposure to meaning-focused communication rather than formal instruction (Dulay & Burt 1974; Felix 1981; Prabhu 1987; Schwartz 1993; Zobl 1995).
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.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it