Bibliographic record
Abstract
One of the most challenging issues facing academic conferences and educational institutions today is plagiarism detection. Typically, these entities wish to ensure that the work products submitted to them have not been plagiarized from another source (e.g., authors submitting identical papers to multiple journals). Assembling large centralized databases of documents dramatically improves the effectiveness of plagiarism detection techniques, but introduces a number of privacy and legal issues: all document contents must be completely revealed to the database operator, making it an attractive target for abuse or attack. Moreover, this content aggregation involves the disclosure of potentially sensitive private content, and in some cases this disclosure may be prohibited by law. In this work, we introduce Elxa, the first scalable centralized plagiarism detection system that protects the privacy of the submissions. Elxa incorporates techniques from the current state of the art in plagiarism detection, as evaluated by the information retrieval community. Our system is designed to be operated on existing cloud computing infrastructure, and to provide incentives for the untrusted database operator to maintain the availability of the network. Elxa can be used to detect plagiarism in student work, duplicate paper submissions (and their associated peer reviews), similarities between confidential reports (e.g., malware summaries), or any approximate text reuse within a network of private documents. We implement a prototype using the Hadoop MapReduce framework, and demonstrate that it is feasible to achieve competitive detection effectiveness in the private setting.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.069 |
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".