Bibliographic record
Abstract
Text recycling—commonly referred to as self-plagiarism—is an issue that is currently garnering considerable attention with regard to its acceptability as a practice and questions of when, where, and how much of it can be permissible. Although the problem of self-plagiarism or excessive text recycling can, in the opinion of some, be circumvented by paraphrasing and the reordering of text, the practice does not constitute a legitimate means to generate new and original text. A possible means to moderate the problem of text recycling that is strongly recommended is a declaration statement explicitly stating and identifying the use of recycled text. Further problems with text recycling relate to questions as to who is the progenitor of any recycled text in question and therefore who is the owner, in a moral sense, of the text under scrutiny in cases of changing sets of authors. This leads to concerns over insufficient author attribution. On the other hand, excessive attribution can result if a too conservative mindset is adopted. Due care and cognizance of excessive/insufficient attribution are necessary to avoid such problems as well as a recognition of the concept of text ownership as described herein. Such concerns are not limited to text recycling but are present also for other types of contributions to a publication covering both mundane physical contributions (e.g., supply of materials, organisms, or apparatuses) and the continuing deployment of previously espoused or established metaphysical contributions (e.g., ideas, hypotheses, strategies, or concepts or the instigation of projects).
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 | 0.116 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.020 | 0.113 |
| Scholarly communication | 0.023 | 0.042 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.026 | 0.018 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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".