The Impact of the Russian War against Ukraine on the German Hydrogen Discourse
Bibliographic record
Abstract
This contribution delves into the transformative effects of the Russian–Ukrainian war on the discourse surrounding German hydrogen. Employing structural topical modeling (STM) on a vast dataset of 2192 newspaper articles spanning from 2019 to 2022, it aims to uncover thematic shifts attributed to the Russian invasion of Ukraine. The onset of the war in February 2022 triggered a significant pivot in the discourse, shifting it from sustainability and climate-change mitigation to the securing of energy supplies through new partnerships, particularly in response to Russia’s unreliability. Germany started exploring alternative energy trading partners like Canada and Australia, emphasizing green hydrogen development. The study illustrates how external shocks can expedite the uptake of new technologies. The adoption of the “H2 readiness” concept for LNG terminals contributes to the successful implementation of green hydrogen. In summary, the Russian–Ukrainian war profoundly impacted the German hydrogen discourse, shifting the focus from sustainability to energy supply security, underscoring the interconnectedness of energy security and sustainability in Germany’s hydrogen policy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".