Bibliographic record
Abstract
L’Europe Unie / United Europe, Paris, is an international peer-reviewed academic journal dedicated to the advancement of European Studies, published in France under French ISSNs [ISSN 2780-8173 (print) et ISSN 2743-4052 (online), ISSN-L 2743-4052]. Inspired by the EU Jean Monnet Programme (Brussels) vision on European Studies, the journal is focused on the EU Studies from multidisciplinary perspectives, such as Wider Europe contemporary history studies, political science, international relations, EU law, EU external action, EU Neighbourhood Policy, Wider Europe society and culture, EU and its Southern and Eastern Neighbourhood. The journal welcomes interdisciplinary approaches and regional perspectives, promoting scholarly diversity and academic innovation. The editorial and scientific board is composed of distinguished scholars from the EU and beyond, including France, Belgium, Portugal, Romania, Estonia, Tunisia, Algeria, Moldova, China. It has published good authors from 3 continents and countries, such as the Netherlands, Belgium, Portugal, Italy, France, Austria, Romania, Estonia, Lebanon, Tunisia, Algeria, Moldova, Ukraine, Georgia, Bangladesh, Vietnam, China, Canada, South America, and more. Submission and Publication Process Submissions must be original, unpublished, and not under review elsewhere. They are accepted in English or French, and must comply with the formatting instructions published on the journal website https://leuropeunie.com.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.030 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.209 | 0.089 |
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".