Bibliographic record
Abstract
In 2002, RMIT University trialled Turnitin (Barrie 1996) a text matching software package to assist in the identification of plagiarism. Turnitin enables access to databases of text stored digitally and provides a means of comparing student submissions. Subsequent to the initial use of Turnitin by staff, a pilot was conducted during which student groups had access to the software to check their submission drafts. Now student assessments whether online or on-campus can be run through the detection software. In this paper, we discuss the process and practices of using plagiarism detection software at RMIT and briefly examine some information gathered from students, both online and on-campus, informal comments regarding their participation in the student upload pilot. From these comments some suggested improvements to the implementation process are discussed. Some directions for future research into student use of Turnitin are also suggested. In recent years, the Schools of Computer Science and Information Technology (CS&IT) and Business and Information Technology (BIT) have spear-headed trials of the use of plagiarism detection software, as well as implementing processes, procedures and workshops for explaining and dealing with academic integrity. This has possibly occurred because most of their student submissions are electronic and therefore amenable to use of copy detection software, or because the staff are well aware of and interested in the technologies involved. Dealing with the numerous cases of plagiarism found by the software has posed difficult questions for both Schools and the University, and is the main issue addressed in this paper.
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 | 0.104 | 0.323 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.016 | 0.024 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".