Clash of Confidence and Responsibility in Scientific Publishing
Bibliographic record
Abstract
Scientific publishing is a highly responsible enterprise that involves shared responsibilities between the authors and the publisher. It is based on mutual trust and on the principles of respect of freedom of expression of ideas. The author is responsible for the content of the article and for the truthfulness of the affirmations while the publisher verifies the formal coherency of the articles and is seldom engaged in the verification of the truthfulness of the original content. Publishing bad science is damaging to the scientific community and society as a whole. It has been shown that there are scientific journals that publish without much control over the form and content of the papers. Such journals usually have low impact on the scientific community. Damage that is made by publishing bad papers and bad research in an unimportant journal is small in comparison to the damage that may be and is often produced if the article is well written but contains trivial results and unsound conclusions and yet is published in a journal of high reputation. Some measures are proposed that could, by improving the reviewing procedure, also affect the quality of the publishing of science. It would also help if, when judging the scientific value of an article, the scientific community were to pay less attention to the fame of a journal or to various quality indicators, but consider more directly the quality of the research itself.
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 | 0.108 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.033 | 0.026 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".