Science in the European Union – “Before and After” A Scientometric Approach to Measure the Structural Transformation of Science in Central and Eastern Europe after the Political-Economical Changes
Bibliographic record
Abstract
This scientometric study compares the scientific structures of the former EU15 and the ‘newcomer’ EU countries. Bibliometric indicators are used to plot the EU countries' scientific patterns based on subject fields and European co-publication maps over time. The study also investigates some peculiarities of certain EU countries' scientific journal usage.Cette étude scientométrique compare les structures scientifiques des premiers 15 pays et des « nouveaux » pays de l’UE. Des indicateurs bibliométriques sont utilisés pour cibler les modèles scientifiques des pays de l’UE en ce qui concerne les domaines d’intérêt et la représentation graphique des co-publications au fil du temps. Cette étude examine également quelques particularités de l’utilisation de certains périodiques scientifiques des pays de l’UE.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.087 |
| Science and technology studies | 0.000 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".