Presenter: Werner Paulus, University Hospital Muenster, Muenster. Publishing A High-quality, Non-commercial Neuropathology Journal Without a Publisher: The First Nine Months
Bibliographic record
Abstract
Scholarly communication faces increasing economical and ethical challenges, including pricing policies and overbearing behavior of commercial publishing houses. Based on the hypothesis that a diamond open access neuropathology journal of a high scientific and technical quality can be run entirely by neuropathologists, we launched Free Neuropathology (FNP; freeneuropathology.org) in January 2020. Classical publisher activities, such as copyediting, layout, website maintenance, and journal promotion, are undertaken by neuropathologists and neuroscientists using free open access software. The journal is free for both readers and authors, and papers are published under a Creative Commons BY SA licence, where copyright remains with the authors. Based on 26 articles published by August 2020, it takes FNP 11.1 days from submission to first, and 19.9 days to final, decision. High-quality copyediting, layout, and online publishing in the final format is accomplished in only 8 days. Absence of a commercial publisher enables prioritization of democratic and scientifically-driven decisions on editorial structure, website design, journal promotion, paper formatting, special article series, and number of accepted papers. This new model of journal publishing, which returns the control of scholarly communication to scientists, will be of interest to neuropathologists and wider scientific community alike. Learning Objectives Summarize the current state and driving forces behind commercial and non-commercial scientific publishing in neuropathology. Describe the advantages and challenges of a non-commercial publishing platform for neuropathology.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.448 | 0.322 |
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".