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Record W4214683432 · doi:10.1075/aals.5.18tur

Plagiarism

2008· book-chapter· en· W4214683432 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAILA applied linguistics series · 2008
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.929
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.038
GPT teacher head0.267
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it