European Journal of Taxonomy: A Public Collaborative Project in Open Access Scholarly Communication
Bibliographic record
Abstract
Most natural history institutions in Europe have been scientific publishers sincetheir foundation and have a long scholarly publishing tradition. Nowadays, they areconfronted with rapid technological developments and face complex strategic andtechnical questions related to visibility, access, format, and the financial structure oftheir titles. These issues require a common vision and an international strategy toensure that the community acts in a consistent and coordinated way. A consortiumof institutions is thus launching the European Journal of Taxonomy to provide analternative public open-access business model, where neither authors nor readers haveto pay fees for subscriptions or publication. This paper focuses on the benefits for theinstitutions on taking greater control over their communication process.
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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 | 0.065 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".