Emeritus Professor, Canada’s Open University,
Bibliographic record
Abstract
Conflicting academic and cultural perspectives hinder international awareness of plagiarism and the development of policies and practices for dealing with it. Research into the extent of the problem currently and over time is needed to counter this. Two case studies are presented, analysing the self-justifications given by an habitual plagiarist, and the repeated plagiarism of a piece of plagiarised material over a ten-year period. Emphasis is placed on the exclusion methods provided by the Turnitin.com service in analysing content originality. Identifying the origins of content over time is likened to the cautious approach used by genealogists in tracing family history links. The findings of such research need to be disseminated in order to explain to international collaborators why plagiarism is not cross-culturally acceptable. Similes such as that between plagiarism and genealogy research can help in explaining plagiarism to students. Statement of the problem Perspectives on plagiarism range from the view that it is a reprehensible behaviour that should be prevented, to the tolerant view that it can be justified in particular cultures and academic activities. These relatively simplistic viewpoints deter discussion of the underlying reasons for
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.153 | 0.034 |
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".