Protecting students' intellectual property in the web plagiarism detection process
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
<p class="AbstractText">Learning management systems (LMS) play a central role in communications in online and distance education. In the digital era, with all the information now accessible at students’ fingertips, plagiarism detection services (PDS) have become a must-have part of LMS. Such integration provides a seamless experience for users, allowing PDS to check submitted digital artifacts without any noticeable effort by either professor or student. In most such systems, to compare a submitted work with possible sources on the Internet, the university transfers the student’s submission to a third-party service. Such an approach is often criticized by students, who regard this process as a violation of copyright law. To address this issue, this paper outlines an improved approach for PDS development that should allow universities to avoid such criticism. The major proposed alteration of the mainstream architecture is to move document preprocessing and search result clarification from the third-party system back to the university system. The proposed architecture changes would allow schools to submit only limited information to the third party and avoid criticism about intellectual property violation. <br /><br /></p>
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 | 0.025 | 0.007 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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