The Poehlman case: understanding and indexing ethical problems in scientific journals
Bibliographic record
Abstract
Ethical problems in scientific publishing lead to complex issues for indexers. It is important for indexers of both scientific journals and literature databases to understand ethical problems and the ‘cascade’ of publications that can result. Common problems involve authorship issues, duplicate publication (also called ‘self-plagiarism’ or ‘redundant publication’), plagiarism and scientific misconduct. These can lead to corrections (in minor cases), expressions of concern (while an investigation is under way) and eventually retraction (‘unpublishing’ the article). An example is provided of an article containing falsified and fabricated data, published in Annals of Internal Medicine in 1995. The discovery of data fabrication led to several linked publications, including a retraction, a letter from the author, an editor’s note, an editorial and letters to the editor. The MEDLINE indexing of these publications is provided, showing how they were linked to each other. In such complex cases involving many publications over several years, finding all associated publications and making a diagram can help in indexing all items comprehensively, making clear their interrelationship.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.042 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.013 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".