Studying Tense in Classroom Discourse on the Base of Hallidayian Systemic Functional Grammar (SFG)
Bibliographic record
Abstract
In functional approach to interpersonal metafunction, Halliday defines clause as a unit of exchange, with two main constituents called Mood and Residue. Mood is composed of Subject and Finite. Subject is invested with modal responsibility whereas finite realizes primary tense and modality. Residue includes predicator, complement and adjunct. Secondary tense is expressed throughout predicator while Complement and adjunct just add additional but unnecessary information to clause meaning. Derived from theoretical framework outlined above, present research tries its best to determine a kind of agreement between primary and secondary tenses in gender talk of ELT classrooms and its relation to bilingual language learners’ proficiency in their foreign language learning. About twelve hours of oral conversation between students and teachers from eight randomly selected classrooms are recorded and transcribed, resulting to 3288 clauses. Our findings show that both genders use simple present tense as primary one in their talks with high frequency to refer to events happening in present. It is found out that simple present tense is used much more for holding an unmarked grammatical structure; whereas using other temporal structures desire much more grammatical competence and subsequently are used with low frequency. This may result into students’ non-proficiency in grammar skill, making them use different and also wrong tenses in different given temporal situations in high levels of foreign language learning.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".