Re-framing writing (support): centring audience and purpose in a community nursing course
Bibliographic record
Abstract
This presentation examined the collaboration between our Learning Development team and a community nursing course. It began with the question; “Are our demands of students concerning paraphrasing and referencing reasonable?” The assignment was a formal report on a semester-long group project where students partnered with a community agency. The coordinators worried that students (and lecturers) were over emphasising referencing and the technicalities of paraphrasing, to the detriment of engagement with the community nursing process itself. Our LD team eventually realized that the problem was not one of expectations, but rather a genre-audience mismatch. Although the assignment was called a report, the emphasis on integrating scholarly sources made it more like an academic essay, and the tone and length of the report limited its practical use by most partner agencies. Over time, by emphasizing genre, audience and purpose, we have contributed to a gradual loosening of the hold on the original report format. Last year, we provided feedback on a range of digital deliverables, including infographics, videos, and mind maps, each one designed to meet the specific partner agency’s needs. Our model of providing feedback on the report during one-hour in-person meetings has also evolved into a flexible combination of synchronous and asynchronous collaboration with students. We continue to guide students towards thoughtful, transparent source use, but the conversations around referencing and paraphrasing are now more holistic. In this presentation, we’ll share how our discipline-external perspective has supported meaningful student learning about authentic (and impactful) writing for different contexts.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".