Bibliographic record
Abstract
In spite of Krashen’s (1985) claims that the only way to acquire a second language is through non-stressful comprehensible input, Swain (1995, 1998, 2005) and others propose that the production of language (speaking or writing), under certain circumstances, is a significant part of the second-language acquisition process. Swain also states that there are three functions of output, and one of these is the noticing or triggering function, in which through producing output, learners become aware of their linguistic knowledge. This study examines the role and effectiveness of output – in particular, the noticing function of language output – in developing the writing skills of an English as a Foreign Language (EFL) student from Saudi Arabia. The notice function enables the student to identify lexis and grammar problems in his writing. Data collection for the study was conducted in three stages: In Stage 1 the participant, who studies English at Latrobe Language Centre in Level 4A, wrote three paragraphs in response to illustrated questions. In Stage 2, the participant compared his original writing to model paragraph feedback tools. In Stage 3, the student rewrote his original paragraphs based on what he noticed in Stages 1 and 2. This methodology demonstrated the aspects of language that a second-language learner noticed while forming a paragraph on his own. It also illustrated what the participant noticed when he compared his writing to a model and what changes he made to his writing, as a result. It pushed the learner to create a modified output, leading to development of his writing skills in second-language acquisition.
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 | 0.025 | 0.170 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 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 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".