Bibliographic record
Abstract
In a rapidly evolving world of technology, it has become easier than ever for students to find the answers to or even have their assignments completed online. Critical thinking and aspects of information literacy have sometimes been replaced by a quick Google search or use of rephrasing software. One-way educators try to counteract this from an academic integrity standpoint is to educate students about academic misconduct. But can we take it a step further? The Assiniboine Community College [ACC] Library has committed to working with colleagues to offer assignment individualization. Rather than simply pointing instructors to the literature which recommends this strategy, Library staff collaborate with instructors and the broader Learning Commons to provide this collaborative service. With each student completing a customized assignment, several forms of academic misconduct are prevented and transversal skills such as critical thinking and information literacy are built in while approaching academic integrity in a holistic way which is anchored in teaching and learning. Having successfully completed assignment individualizations for multiple instructors and programs, ACC’s Library Technician Academic Integrity/Copyright Officer will share successes, challenges, and recommendations for attendees looking to offer this service.
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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 | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.090 | 0.026 |
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".