Engaging with dialogic alternatives in ESL argumentative essays: systemic functional linguistic and teacher perspectives
Bibliographic record
Abstract
This study explores the patterns of intersubjective stance in short argumentative essays written by students in a pre-university level ESL course.It adopts the analytical framework of engagement from systemic functional linguistics, which describes the options of interpersonal meanings as resources to engage with different knowers and their views.Previous research adopting this linguistic scope has mainly focused on analysis of texts.The current study attempts to connect the linguistic analysis with perspectives of an ESL writing teacher.A multi-phased mixed methods design was adopted to both analyze the engagement patterns in the student texts, and to discuss the text analysis with the course teacher in an interview.The analysis of engagement reveals patterns that contribute to an effective, dialogically engaged intersubjective stance, with frequent, diverse, and strategic deployment of heteroglossic meanings.These patterns were recognized by the teacher as valued elements of "nuance" in argumentative writing.Interestingly, the less dialogically engaged, "assertive" stance was also encouraged and even considered more suitable for the pedagogical context, given the emphasis on clear, coherent, and concise expression of ideas in the essay assignment, the students' level of lexico-grammatical control, and cultural differences.The findings also include potentially problematic engagement patterns, as well as the engagement-related terms and notions used by teacher in the interview.The text and teacher perspective findings have implications for preuniversity ESL writing pedagogy, as well as for theorization and analysis of engagement.I owe a deep debt of gratitude to my thesis supervisors, Dr. Beverly Baker and Dr. Carolyn Turner, for their constant support from the very beginning of this project, their careful guidance throughout this journey, and their patience with my work.They provide me with not only new insights and perspectives to second language education, but also role models in being a rigorous researcher and a devoted educator who guides students forward with both expertise and care.I am truly thankful to all the anonymous student and teacher participants who made my research ideas happen, especially the teacher, for introducing me to his class, for his generosity with time and his valuable comments in the interview.I would also like to thank Carolyn Samuel and other writing experts for their encouragement and advice concerning my research population.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".