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Researcher profile

Nicholas Weber

Nicholas Weber contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Digital LibrariesArtificial IntelligenceComputation and LanguageSoftware Engineering

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Published work

3 published item(s)

preprint2026arXiv

Code Contribution and Credit in Science

Software development has become essential to scientific research, but its relationship to traditional metrics of scholarly credit remains poorly understood. We develop a dataset of approximately 140,000 paired research articles and code repositories, and a predictive model that matches research article authors with software repository developer accounts. We use this dataset to investigate how software development activities influence credit allocation in collaborative scientific settings. Our findings reveal significant patterns distinguishing software contributions from traditional authorship credit. We find that $\sim$30\% of articles include non-author code contributors -- individuals who participated in software development but received no authorship recognition. While code-contributing authors provide a $\sim$4.2\% increase in article citations, this effect becomes non-significant when controlling for domain, article type, and open access status. First authors are significantly more likely to be code contributors than other author positions. Notably, we identify a negative relationship between coding frequency and scholarly impact metrics. Authors who contribute code more frequently exhibit progressively lower h-indices than non-coding colleagues, even when controlling for publication count, author position, domain, and article type. These results suggest a disconnect between software contributions and credit, highlighting important implications for institutional reward structures and science policy.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering

Effective personalized question answering (PQA) in language models requires grounding responses in the user's underlying intent, where intent refers to the implicit ``why'' behind a query beyond its explicit wording. However, existing approaches to intent-aware personalization rely on multi-turn conversational context or rich user profiles, and do not explicitly model user intent during the reasoning process. This limits their effectiveness in single-turn settings, where the user's latent goal must be inferred from minimal input and integrated into the thinking and reasoning process. To bridge this gap, we propose IAP (Intent-Aware Personalization), a reinforcement learning framework that trains models to infer implicit user intent directly from a single-turn question and incorporate it into thinking steps through a tag-based schema for generating personalized, intent-grounded answers. By optimizing intent-aware answer trajectories under a personalized reward function, IAP reinforces generation paths that make implicit user intent explicit and produce responses that better align with the user's underlying goal. Through experiments on the LaMP-QA benchmark across six models, IAP consistently outperforms all baselines, achieving an average macro-score gain of around 7.5\% over the strongest competitor, demonstrating that modeling implicit user intent within the training objective is a promising direction for PQA.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Councils in Action: Automating the Curation of Municipal Governance Data for Research

Large scale comparative research into municipal governance is often prohibitively difficult due to a lack of high-quality data. But, recent advances in speech-to-text algorithms and natural language processing has made it possible to more easily collect and analyze data about municipal governments. In this paper, we introduce an open-source platform, the Council Data Project (CDP), to curate novel datasets for research into municipal governance. The contribution of this work is two-fold: 1. We demonstrate that CDP, as an infrastructure, can be used to assemble reliable comparative data on municipal governance; 2. We provide exploratory analysis of three municipalities to show how CDP data can be used to gain insight into how municipal governments perform over time. We conclude by describing future directions for research on and with CDP such as the development of machine learning models for speaker annotation, outline generation, and named entity recognition for improved linked data.

0 citations0 reviewsNo code linkedOpen access