Escritores-fantasma e comércio de trabalhos científicos na internet: a ciência em risco
Bibliographic record
Abstract
UNLABELLED: Frauds in scientific production are not a rare phenomenon, even in the medical field. Among these frauds are some types of authorship misconduct, such as plagiarism and ghostwriting sponsored by pharmaceutical industries. Another type of misconduct, which is particularly detrimental to science, is the e-commerce of scientific works, which has been growing and frequently shown in the press. OBJECTIVE: To analyze the e-commerce of scientific papers and the means by which these services are offered. METHODS: Eighteen Brazilian web sites that offer elaboration of scientific papers were selected. A request for the elaboration of a final essay for a forged post-graduate course was sent to each of them. The research requested had already been completed, consequently technical, ethical and bibliographical characteristics were already known to the author. RESULTS: Ten enterprises accepted the order and, except for one, they have not objected to the conditions imposed: Field research, approval by an ethics committee on research and use of the Vancouver norms. Six have not replied and two have not accepted the order alleging that they had no co-workers available for the task. CONCLUSIONS: E-commerce of scientific papers is a fact which can negatively interfere in the ethical, scientific and professional development of graduate and post-graduate students, as well as in scientific production by adulterating data and information found in literature. A new approach is recommended, especially when evaluating final essays.
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 | 0.027 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.025 | 0.014 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".