Bibliographic record
Abstract
Abstract Academic misconduct frequently occurs because developing academic writers lack both knowledge about the conventions for writing from sources and procedural skills for applying this knowledge. Paraphrasing is a particularly underdeveloped skill among students in higher education. This chapter illustrates how findings from existing quality assurance processes are supporting a revised approach to paraphrasing instruction by the writing strategist team at a Canadian undergraduate university. The new approach underlines the interpretive nature of paraphrasing and the agency of the student writer. By focusing less on the technical aspects of paraphrasing and more on its rhetorical purposes, we aim to foster among students a deeper level of engagement with texts, a more nuanced awareness of intertextuality, and recognition of the role disciplinary conventions play in writing from sources. Our vantage point as professionals working with students in a wide range of disciplines affords us unique opportunities to be campus changemakers. If we can encourage recognition that paraphrasing instruction must extend past first year composition courses and one-off workshops, and if we can help instructors seize opportunities to provide students with feedback on their paraphrasing, students will move beyond patchwriting and towards writing from sources with more confidence and integrity.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".