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Expanding the HORIZON: The Strategic Repurposing of Europe's Research Framework.
Utrecht University · MSc Applied Data Science · July 7, 2025. Written in cooperation with Open Future.
I investigated whether Horizon Europe's climate-related research portfolio is undergoing a rhetorical shift toward "strategic autonomy" framing. I built an LLM-based pipeline on ~53k SEDIA project descriptions, introduced a funding-weightedStrategic Alignment Scoreon ~4k climate-aligned projects, and releasedsedia-api-fetchersfor reproducible EU funding data access.
I designed a computational framework around large language models: I combined semantic similarity (embeddings), transformer-based classification, and zero-shot inference to detect discursive drift from green language toward strategic framing. The Strategic Alignment Score is tracked over time weighted by funding volume, so "green-to-strategic" drift is measurable in euros, not just word counts. I built the corpus from roughly53,000project descriptions from the SEDIA database and concentrated the main analysis on4,049high-confidence climate-aligned projects.
Key findings: I report astatistically significantincrease infunding-weightedstrategic alignment when comparing the Horizon 2014–2020 period with Horizon Europe 2020–2027. Within Horizon Europe itself, I donotsee the same shift,includingaround the 2022 Russian invasion of Ukraine. Unweighted strategic framing scores stay largely flat over time; the action shows up inwhichprojects get money, not in a uniform language change across all project texts.
I developed and released the open-source Python packagesedia-api-fetchersso others can reproduce automated retrieval and processing from the EU funding APIs: pagination, rate limits, temporal partitioning past hard result caps, change detection, and pandas-friendly normalization.
Python, transformers, embeddings, NLI (MNLI), pandas, SEDIA / EU APIs, reproducible research, LaTeX.