The last bite was deadly – About responsibility in scientific publishing
Bibliographic record
Abstract
Some open access journals are believed to have devaluated the highly respected image of the scientific journal. This has been, it is claimed, verified. Yet the project we believe failed and we show why we think that it failed. The study itself was badly conducted and the report, which Science published, was itself a perfect example of "bad science". If the article that was published in Science were to be taken as one of the "test" articles and Science as a victim journal (a perfect control though), the study would show the opposite of what author concluded in his paper: 100% of the controls (normal non-open access journals, in the present study this was Science) accepted the "bait" paper for publication, while in the experimental group only about 60% (open access journals) accepted the bait paper for publication. The conclusion is that, with respect to non-open access and open access, the probability of accepting pseudoscience is well in favor of this being done by a non-open access journal. Since this interpretation is based on some facts that were not included in the project itself, the only warranted result of this study would be that nothing could be concluded from it. It is concluded that the method that Bohannon used was heavily flawed and in addition immoral; that the report that was published by Science was inconclusive and that the act of publishing such report cannot be morally justified either. Various methods to improve the quality of published papers exist but scientific fraud with "good intentions" as a method to promote scientific publishing should be avoided.
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 | 0.123 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.087 |
| Scholarly communication | 0.022 | 0.028 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".