The implementation of research security policies in Germany: exploring policy narratives across governance levels
Bibliographic record
Abstract
For decades, European Union (EU) Member States have promoted openness and collaboration in science. However, amidst a changing geopolitical world order, they increasingly view international research collaborations as a possible gateway for foreign interference. As a result, several Member States have tightened international research collaborations through “research security” measures like visa restrictions for foreign researchers. So far, the implementation of these measures remains under-researched. We address this blind spot by using the narrative policy framework as a theoretical lens to investigate how the implementation of research security policies is narrated across the micro- (individual), meso- (organisational) and macro- (governmental) levels in Germany. Based on a mixed-methods analysis, we show that there are currently few linkages between policy narratives across governance levels. This narrative inconsistency creates uncertainties for micro-level actors that are tasked with policy implementation, thus endangering policy effectiveness. Conceptually, our study introduces critical nuances that help refine our understanding of how policy narratives evolve. Specifically, it demonstrates that rather than being implemented in a linear way from design to execution, research security narratives are created in a space of narrative autonomy in terms of sentiments, terminology and suggestions for implementation.
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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 | 0.055 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".