@inproceedings{rovera-etal-2026-research,
title = "Research Design Tracking and Assessment for the Social Sciences",
author = "Rovera, Marco and
Burlacu, Sergiu and
Cappelletti, Dominique and
Tomelleri, Alessio and
Marzadro, Sonia and
Bazzoli, Martina and
Tassi, Annalisa and
Gagete-Miranda, Jessica",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.484/",
pages = "11105--11135",
ISBN = "979-8-89176-459-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Research Design Tracking and Assessment for the Social Sciences
%A Rovera, Marco
%A Burlacu, Sergiu
%A Cappelletti, Dominique
%A Tomelleri, Alessio
%A Marzadro, Sonia
%A Bazzoli, Martina
%A Tassi, Annalisa
%A Gagete-Miranda, Jessica
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F rovera-etal-2026-research
%X 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.
%U https://aclanthology.org/2026.emnlp-main.484/
%P 11105-11135
Markdown (Informal)
[Research Design Tracking and Assessment for the Social Sciences](https://aclanthology.org/2026.emnlp-main.484/) (Rovera et al., EMNLP 2026)
ACL
- Marco Rovera, Sergiu Burlacu, Dominique Cappelletti, Alessio Tomelleri, Sonia Marzadro, Martina Bazzoli, Annalisa Tassi, and Jessica Gagete-Miranda. 2026. Research Design Tracking and Assessment for the Social Sciences. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 11105–11135, Budapest, Hungary. Association for Computational Linguistics.