academic publishing teams

Abstract: Author Name Disambiguation benchmarks are predominantly derived from closed infrastructures that may not reflect conditions in open bibliographic environments. We present OC-AND, an open citation-enriched benchmark for AND constructed by remapping the WhoIsWho dataset with bibliographic and citation metadata from OpenCitations. The dataset is produced through a reproducible five-stage pipeline involving DOI verification, metadata validation, OpenCitations enrichment, and author-centric transformation. OC-AND retains the WhoIsWho ground-truth files and partition structure unchanged, while its publication files are restricted to publications that could be matched to a validated DOI and an OpenCitations record. It introduces realistic characteristics of open environments: heterogeneous metadata completeness, absent abstracts and affiliations, asymmetric citation coverage, and sparse graph connectivity. The dataset contains author records with associated citations, enabling evaluation of AND methods in settings closer to real-world open scholarly infrastructure scenarios. OC-AND is released as open data with complete provenance documentation and pipeline code, supporting reproducible research and the development of citation-aware disambiguation approaches.

Abstract: The Ising model is extended to the Potts model for multinomial data. We introduce a Rater Ising-Potts model that uses agreement indicators between pairs of raters and category labels, with weights derived from LLM embeddings. The model does not presuppose ordered category thresholds or equidistant scoring; instead, it focuses directly on pairwise agreement among raters and assigns category-specific positive weights, making it particularly suited for multi-category scoring reliability when raters evaluate responses using a scoring guide. We demonstrate the model's effectiveness on diverse constructed-response tasks, including balanced short-answer items and more challenging, imbalanced essay prompts from the AERA dataset. Across these settings, the model achieves strong agreement with human scores, with the vast majority of misclassifications occurring between adjacent score levels, confirming its ability to preserve the ordinal structure of scoring rubrics without imposing rigid assumptions. A practical similarity normalization and optional power transformation is introduced as a tunable preprocessing step that sharpens semantic distinctions and can be adapted to different datasets. These findings suggest that LLM-derived semantic similarities, combined with this parsimonious Potts-type formulation and flexible similarity scaling, offer a robust and interpretable framework for reliability auditing in educational assessment contexts. Extensions to multiple raters and hierarchical rating processes are discussed.

Abstract: Citation-based measures of scientific influence typically treat citations as uniform signals, ignoring the different roles that cited works play in a paper's contribution. We introduce contribution-based credit scoring for research articles: a structured citation analysis that decomposes a paper's credit between its own original contribution and the prior work it builds on. Motivated by a cooperative-game view of scientific credit, we propose the contribution tree, a hierarchical framework that conserves importance across the document structure and separates original from citation-derived contribution. To make this framework scalable, we use LLMs as noisy comparative estimators of local importance. We further extend the model to article collections by propagating contributions through weighted citation graphs, yielding corpus-level contributions and normalized influence scores. Our experiments suggest that our framework captures contribution signals beyond surface-level heuristics. Our code is available at https://github.com/sanaebrahimi/Importance_Scoring/