SEISMICA: OPEN SCIENCE AND COMMUNITY BUILDING IN A NEW DIAMOND OPEN ACCESS JOURNAL
Bibliographic record
Abstract
Seismica, a pioneering diamond open access journal in Seismology and Earthquake Science, launched in July 2022. Seismica is an independent journal supported by McGill University, designed and built by a global community of researchers with the aim of making scientific research freely available. Our international team of over 40 people includes disciplinary experts as well as specialists in open science and data; equity, diversity and inclusion; outreach and communication; and digital media and branding. Volunteers cover traditional editorial roles as well as the journal’s full-time management and operation (including technical support, copy editing, branding and communications). Now in its second year of publication, Seismica has evolved as both a journal and a community dedicated to transparency in science, and supporting not only open access articles, but also a fully open publication process. Beyond traditional research articles, Seismica publishes an innovative set of peer-reviewed reports including fast reports, null results/failed experiments, software reports, and instrument deployment/field campaign reports; we also require the sharing of related data and code. This initial year of growth has included the development of of our own reproducible workflows on the backend, the publication of a special issue in response to the Turkiye earthquakes of February 2023, and much work behind the scenes fostering community, mentoring new editors, opening peer review, investigating future funding and planning for sustainable succession. This presentation will demonstrate how Seismica contributors have responded to community needs and changing expectations in the field while gaining invaluable professional experience along the way.This presentation was supported by a Presentation Grant from the Librarians Association of the University of California (LAUC).
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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 | 0.070 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.045 | 0.030 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.076 | 0.044 |
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".