Tektonika: breaking barriers in scientific publishing one manuscript at a time
Bibliographic record
Abstract
Science, without effective dissemination, has a very short life and little impact. Yet, most scientific research is hidden away behind exclusive and expensive paywalls imposed by traditional publishers. Tektonika is an Earth Science community-led diamond open-access journal (DOAJ: free for authors, free for readers) publishing peer reviewed research in tectonics and structural geology. It is a grass-roots initiative driven by the enthusiasm and devotion of a wide and diverse spectrum of Earth Scientists from around the globe, intended to help shape a new landscape for publishing in the geosciences. Since its debut at EGU2022, Tektonika has experienced steady growth, fueled by a consistent stream of manuscript submissions. Tektonika’s success reflects broader trends among community-driven DOAJs, demonstrating their ability not only to survive but to flourish. The strong support of the Earth Science community has been instrumental—from authors entrusting their work to the journal, to individuals amplifying its reach via social media, and volunteers contributing to editorial tasks, peer review, and the formatting of accepted articles. Tektonika stands as a testament to the power of collective effort in transforming scientific publishing.
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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 | 0.145 | 0.555 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.064 | 0.051 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.057 | 0.059 |
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".