The launch of Seismica: a seismic shift in publishing
Bibliographic record
Abstract
Seismica, a community-run Diamond Open Access (OA) journal for seismology and earthquake science, opened for submissions in July 2022. We created Seismica to support a shift to OA publishing while pushing back against the extreme rise in the cost of OA author processing charges, and the inequities this is compounding. Seismica is run by an all-volunteer Board of 47 researchers who fulfil traditional editorial roles as well as forming functional teams to address the needs for technical design and support, copy editing, media and branding that would otherwise be covered by paid staff at a for-profit journal. We are supported by the McGill University Library (Québec, Canada), who host our website and provide several other services, so that Seismica does not have any income or financial expenditures. We report the process of developing the journal and explain how and why we made some of the major policy choices. We describe the organizational structure of the journal, and discuss future plans and challenges for the continued success and longevity of Seismica.
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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 | 0.111 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.060 | 0.022 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".