Characterizing the ‘Focus-on-form’ as the SLA Classroom Mediation Strategy: Should This Be ‘Grammaticality’- or ‘Textuality’-oriented?
Bibliographic record
Abstract
Grammar has always been considered by language learners as well as by those engaged in language education as an essential component of language, and their expectations from and planning for any language education programs have been conditioned accordingly. The definitions of the term grammar and its categories in all languages go back to traditional Latin and Greek grammarians irrespective of their possibly obvious differences and have persisted even now long after the emergence of the scientific study of language which recognizes the unique system of every single language (cf. Saussure, 1916/ 1956). What is grammar and how much is it effective in ‘learning’ an L2, if at all? This paper will examine the commonsensical understanding of the term grammar, i.e. ‘the code-system’ as opposed to ‘grammar’ as ‘a theory of human experience’: an agency construing human experience into meaning (cf. Halliday & Matthissen,2004), i.e. ‘grammaticality’ as opposed to ‘textuality’, arguing that if any recourse to grammar is advocated, as done in Second Language Acquisition (SLA) literature in the form of ‘focus-on-form’ mediation, this ‘form’, rather than being defined in terms of ‘grammaticality’, should be ‘textuality-oriented’ due to the reality that the knowledge accumulated by the learner about the grammaticality is of declarative nature and as such it will not convert into procedural communicative competence. Expanding upon the work done earlier on the topic (cf. Lotfipoursaedi, 2015, 2016, & 2019), the concept of textuality and how its perception by the recipients of a text enables them to handle it will be further discussed and examples of textuality-oriented L2 education pedagogic moves, as the SLA classroom mediation strategies will be examined.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".