Clustering National Open Science and Open Access Policies: A Comparative Analysis of the Research Ethics Standards of European Countries
Bibliographic record
Abstract
In recent years, Open Science (OS) and Open Access (OA) have become integral to European research policy, driven by the need for greater transparency, accessibility, and collaboration in knowledge production. Despite growing support from the European Commission and other supranational actors, national-level implementation remains fragmented and uneven across the continent. This study aims to compare the development and enforcement of OS&OA policies across 29 European countries and to identify clusters of nations with similar policy profiles. To achieve this, 15 binary and ordinal indicators were compiled from public datasets and policy reports. Using hierarchical clustering based on Manhattan distance and Ward’s D2 linkage, countries were grouped into three distinct clusters. Supporting analyses included descriptive statistics, PCA, and radar plot visualisation. The results show a high clustering tendency (Hopkins H = 0.988) and reveal three meaningful groups: (1) countries with limited or symbolic engagement in OS&OA (e.g., Italy, Ireland); (2) moderate adopters with partial institutionalisation (e.g., France, Czech Republic); and (3) leaders with comprehensive, formalised frameworks (e.g., Netherlands, Germany, Spain). Cluster 3 countries fully include FAIR principles, citizen science, and national mandates, while Cluster 1 countries largely lack these advanced elements. These findings underline the structural disparities in OS&OA policy maturity across Europe and support tailored policy support, peer-learning initiatives, and regional alignment efforts within the European Research Area.
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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 | 0.045 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".