Moving the Spotlight from Plagiarism to Academic Integrity in Paraphrasing Instruction
Bibliographic record
Abstract
For higher education students completing research-based assignments, paraphrasing is an essential skill. While instructors often expect students to be reasonably proficient in paraphrasing by the time they finish high school, the reality is that many students arrive at college or university never having experienced explicit instruction in paraphrasing. They have certainly used paraphrasing in their previous academic work, but their understanding of this critical skill rarely goes beyond the basic notion that paraphrasing means “saying it in your own words,” and many believe that synonym substitution is paraphrasing. Once students embark on their post-secondary journey, paraphrasing instruction is still rare, but the stakes are immediately higher. Through dire warnings on course outlines and in assignment instructions, students quickly learn to associate paraphrasing with plagiarism, and the resulting fear can prevent them from becoming excited about joining the academic conversation. In the paraphrasing workshops offered by university and college writing centres, practice opportunities may be limited to short, decontextualized transformation activities, which can inadvertently reinforce the common but misguided belief among students that effective source integration is a matter of skimming the first few pages of a source for a useful target sentence to slot into a pre-existing argument. This session will describe how writing specialists at one undergraduate university are shifting their approach to paraphrasing instruction. Practice activities that prioritize contextualization and writer agency are helping students discover the power of paraphrasing. By de-emphasizing plagiarism and instead focusing on the values of academic integrity, this new approach aims to help students view themselves as members of discourse communities - members who have a responsibility to deeply engage with and fairly represent one another’s work.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchResearch integrity Domain: Methods · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Not applicable | high |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".