Tektonika: one more year of open science 
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 launch at EGU2022, Tektonika has been growing steadily thanks to a constant stream of new manuscript submissions, many of which have already been published as part of the journal’s first two issues (the first compiled in July 2023, and the second in January 2024). In order to meet the increasing demands of running a growing journal, the original team of editors was expanded in 2023 (from 6 to 8 Executive editors, and from 13 to 21 Associate Editors). Despite initial external skepticism, our experience over the last few years mirrors those of our sister journals, proving that community-driven DOAJs can not only succeed but thrive. The community support has been palpable throughout - from those submitting their work for publication, to others helping us reach a wider audience through social media, to the many that volunteer their time to support the editorial work, the review process, and the typesetting and pagination of the accepted research papers.
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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.048 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.202 | 0.151 |
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".