A Systemic Functional Analysis on Texts Written by ESL Learners, and A Text on Daily English Canada Newspaper, and Its Implication for English Teaching and Learning Improvement
Bibliographic record
Abstract
This study aims to reveal characteristics of English language produced by English as Second Language (ESL) Learners in the form of written text. By using a theme and rheme analysis. Within the Systemic Functional Analysis Framework, the writer compared the features of English language utilized by ESL learners and those by native proficient writer of an English Newspaper called English Daily Cananda for their textual meaning. The Analysis comprises analysis of different aspects of language use in a written text which include the analysis of thematic structure, thematic development, and textual cohesion. The result of the analysis shows the distinctive characteristics amongst the three texts from which teachers of English as Second language classes can draw a conclusion in order to design a lesson plan which is more suitable for the students. It is expected that this analysis can provide a way of understanding the limitation of the resources available in students’ mind, and whether or not the students successfully utilize these resources for social purpose, thus gives contribution and implication towards the future direction of the English as Second Language teaching and learning.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".