Defining with Purpose: Connecting Lexicogrammatical Features to Textual Purpose in Authentic Undergraduate Texts
Bibliographic record
Abstract
Abstract This paper showcases the development of linguistically ‐responsive pedagogy in a first ‐year writing course to facilitate students’ recognition of the connection between discrete language features and purpose of definitions. Paraphrasing definitions was chosen as the first textual focus in response to disciplinary instructors’ anecdotes of students talking ‘around’ key terms rather than being precise, and in response to their role in establishing terms within larger texts. Acknowledging the benefit of seeing language as a system, we draw on the research and (simplified) metalanguage of Systemic Functional Linguistics (Derewianka, 2011; Halliday & Matthiessen, 2004; Martin & Rose, 2005). We introduce the foundational relationship between purpose and features, then use the teaching and learning cycle (Rothery, 1994) to scaffold students’ writing development through deconstruction and joint construction. Although definitions are quite specific, the process of determining their purpose, features and usage serves as a foundation for multilingual students’ knowledge and skills, enabling them to respond to the varied linguistic demands of discipline‐specific post‐secondary writing.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".