Bibliographic record
Abstract
The negative connotations of plagiarism as an illegal appropriation of ideas are based on the concept of Intellectual Property. Although Intellectual Property Laws in most countries around the world are specific as to the characterisation of plagiarism as an offence, the extent of plagiarism litigation varies enormously and this variation has a lot to do with the way writers, whose texts are plagiarised, and plagiarists themselves, view the act of being plagiarised or the act of plagiarising somebody else’s text. In countries which fall within the Common Law tradition such as the United Sates, Australia, Canada, Great Britain, plagiarism litigation is extensive and there is a regular offer of linguistic expertise to solve plagiarism disputes. In countries within the Civil Law tradition, like Spain, for example, linguists are still rarely called upon as expert witnesses in plagiarism cases. Plagiarism is multidimensional, as is proved in the number of areas of knowledge affected by it (including literature in all its forms: essay, novel, theatre, poetry), the settings and activities in which it occurs (education, translation), and the contexts in which it is produced (for example, the scope of plagiarism on the Internet is twofold since one can plagiarise directly from the web or use the web as a method to detect plagiarism). As expert witnesses, linguists are frequently asked to give evidence in court to help to decide cases of plagiarism of ideas, linguistic plagiarism, or both. In the first case, the distinction between author’s rights and copyright may be useful, because these two concepts and terms are used differently in different judicial systems. In the second case, it may be important for linguists to come up with theoretical and methodological proposals that help them as legal consultants to find linguistic markers and discourse strategies that will be decisive in plagiarism detection, as well as in establishing prima facie cases. As in any other forensic linguistics contexts, plagiarism is an area where the need to incorporate internal and external validity to the experts’ findings is strongly felt. When giving opinions in court, it has been proven that both qualitative and quantitative approaches to plagiarism detection are valid and complementary, and also that both semantically and statistically expressed opinions may be necessary.
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 | 0.014 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.020 |
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".