Empirical analysis: methodological alignment in EU-funded artificial intelligence research

This record is part of an empirical analysis of methodological alignment in EU-funded artificial intelligence research. The analysis evaluates whether the methodologies actually deployed in the research output that a publicly-funded artificial-intelligence research project produces align with the methodological commitments the project declared at its proposal level. The alignment evaluated in this record is the alignment between the methodological architecture of the publication corpus produced by the project and a reconstructed methodological criterion — the Marcus-tradition criterion of hybrid AI, which specifies architectural integration of symbolic and statistical components such that the statistical layer's outputs are constrained by symbolic structure (or vice versa), the architecture closes the explainability deficit pure-statistical baselines exhibit, and the system demonstrates compositional or causal reasoning beyond pattern-matching. The criterion is reconstructed from the canonical neurosymbolic literature (Marcus 2018, 2020; Garcez & Lamb 2023; Hitzler & Sarker 2022; Sarker et al. 2021; Hamilton et al. 2022) and operationalised across four axes — symbolic_component, integration_pattern, output_type, inspectability — that compose the aggregate marcus_score. In order to ensure the analysis stays at institutional level (avoiding personal attribution to researchers), publications are referred to by an opaque identifier (pub_uid, P001–P075) joinable on pub_uid against the companion record (see relatedIdentifiers); no bibliographic information (titles, author names, venue names, DOIs) is included in this record. The CSV is enriched with year / research_type / disciplinarity / llm_use columns from the companion record's publications table so it is self-contained for analytical consumption. Contents: decision-rules.md documents the per-criterion application protocol; derived-tables/marcus-criterion-scoring.csv carries the 75-row per-publication classification.