Editorial workflow of a community-led, all-volunteer scientific journal: lessons from the launch of Seismica
Bibliographic record
Abstract
Seismica is a community-led, volunteer-run, diamond open-access journal for seismology and earthquake science, and Seismica's mission and core values align with the principles of Open Science. This article describes the editorial workflow that Seismica uses to go from a submitted manuscript to a published article. In keeping with Open Science principles, the main goals of sharing this workflow description are to increase transparency around academic publishing, and to enable others to use elements of Seismica's workflow for journals of a similar size and ethos. We highlight aspects of Seismica's workflow that differ from practices at journals with paid staff members, and also discuss some of the challenges encountered, solutions developed, and lessons learned while this workflow was developed and deployed over Seismica's first year of operations.
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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 | 0.182 | 0.340 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.036 | 0.016 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.019 |
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".