Research Design Tracking and Assessment for the Social Sciences

Abstract:Reliable assessment of causal research designs in the social sciences is critical for evidence-based policy-making, yet has so far relied entirely on manual expert analysis. We introduce Automated Research Design Tracking and Assessment (ARDTrA), a task that involves detecting the research design used in a paper and assessing the quality of its application. We create an expert-annotated dataset of papers covering six families of counterfactual research designs and evaluate the task using a multi-turn RAG-based conversational pipeline. Across four retrieval strategies, four LLMs and six embedding models, we find that passage length is the main driver of performance, explaining 52-66% of the variance. A per-research-design analysis also shows that human and machine difficulty do not align: the designs that prove hardest for the system are not those on which expert annotators disagree most, pointing to two independent sources of task difficulty.

Computation and Language (cs.CL); Computers and Society (cs.CY)

arXiv:2608.27049 [cs.CL]

(or arXiv:2608.27049v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.27049 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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