Using TurnItIn to Run Cheating-Resistant Take-Home Tests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thanks to a lot of criticism, TurnItIn has changed a lot of its settings recently that comply with privacy legislation. In this session, a former academic librarian turned business professor will show and discuss why TurnItIn is a useful tool for avoiding plagiarism. By having the students generate their Similarity Reports themselves, and as many times as they want, faculty are providing a new opportunity to students to self-identify mistaken plagiarism. This proactive, student-driven focus is proving especially helpful for international students who are still new to the Western ideas of plagiarism, sharing credit, and copying works. Furthermore, students themselves are self-reporting to faculty that they feel less pressure to cheat because there is more opportunity for early feedback on their writing at times outside the regular Writing Centre and Library service hours. This presentation includes a copy of the assessment package for Business Case Analyses used by CapU faculty that incorporates the use of TurnItIn to maximize student success and minimize challenges with academic 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it