ALT In library catalogues based on MARC21 bibliographic metadata schema there are about a dozen data elements that catalogers can utilize to record information about the different aspects of translations: whether or not the edition is a translation, source and target language, title of the original work, translator and other contributors, publication data of the original work, literary genre. Libraries however rarely explore the full arsenal, thus to find translation is not a trivial task. The paper describes these important MARC21 data elements, and shows a use case of finding translations in five catalogues (of the Norwegian, Croatian, Polish, Belgian and Dutch national libraries) explaining the differences among the practices, and the results.

ALT With the growth of scientific output, the peer review system is approaching a breaking point: reviewers are increasingly hard to secure, and turnaround times lengthen accordingly. LLM-assisted or LLM-generated peer review has been proposed as a remedy, but how such models would treat manuscripts already published in top-tier journals, the closest thing the current system has to a gold standard of scientific validity, is not known. Here we assembled a corpus of 200 articles, each released under a CC BY 4.0 license and published in 2026, from 20 general-science, life-science, and clinical-medicine journals including Nature, Science, and Cell, and asked four current LLMs (two proprietary, two open-weight) to produce independent peer-review reports of each one under a blinded protocol. Their decisions were overwhelmingly harsh and starkly inconsistent with each other: 605 of 800 reviews (75.6%) recommended major revision or rejection, but the rate at which any single model did so ranged fr

ALT Business students beginning a research project must learn how to identify relevant scholarly publications, distinguish disciplinary sources, construct database searches, and report the decisions behind their literature reviews. These tasks can be difficult because journal classification systems and bibliographic databases organize scholarly information differently. This paper introduces Journal Basket, a browser-based tool developed to help business students conduct journal-bounded literature searches and currently available in beta. Journal Basket enables users to construct journal collections from the Australian Business Deans Council (ABDC) Journal Quality List, choose from 42 business discipline presets, entire ABDC fields or the Financial Times FT50 list, apply journal rating thresholds, and generate search expressions for Scopus, Web of Science and Business Source. Users can also inspect and modify journal collections, download journal inventories, and generate methodological des

ALT Reviewer lottery error describes error due to a small set of peer reviews attending to only some of the issues with a paper that a larger pool of eligible reviewers would raise. We show this error can be reduced by surfacing a checklist of criticisms learned from a historical review corpus. Our approach generates a tree of checklists representing coarsenings of the atomic review claim space, then selects the coarsening that maximizes a theoretically-motivated measure of correlated agreement among reviewers. We realize this approach with an LLM-assisted pipeline and test it on NeurIPS 2021 and ICLR 2022 review data. In simulated review with LLM reviewers, the learned checklist reduces the mean squared error (MSE) of a three reviewer committee by 58% on NeurIPS and 21% on ICLR, and on both venues the checklist with the highest correlated agreement has the lowest error for committees of three or more. In a human study with 20 participants, the learned NeurIPS checklist lowers the MSE of a

ALT A knowledge graph needs a stable address for its evidence even where the literature offers competing classifications and agrees on none. Yet an address chosen for retrieval readily passes for the only classification available. This happens whenever records gathered under one name concern units that differ in boundaries, rank, or the criteria by which they are described or counted. In Sinitic dialectology the same localities recur in schemes whose units differ in membership and rank, and a unit may change its affiliation when the evidence for its diagnosis changes. Two tone counts, each quoted faithfully, may still count different things. The taxonomy of an entire field thus comes to rest on the unstable frame of a literature that contradicts itself. Neither verifying a quotation nor searching for conflicting values detects these differences. I distinguish the working address that gathers candidate evidence, the category that the source defines and the decision that admits a passage to

ALT Scientific progress depends on both developing established knowledge and disrupting it to make way for new ideas. Using the disruption index (D), our previous work showed that large teams tend to develop science, while small teams are more likely to disrupt it. Recent work challenges this finding, arguing that D mechanically depends on reference-list length. Using an alternative measure (oneD), it reaches the opposite conclusion: large teams are more disruptive. Combining Nobel Prize case studies, mathematical analysis, and large-scale publication data, we show that oneD misclassifies developmental contributions as disruptive and introduces the very sensitivity to reference-list length that it seeks to eliminate. The original measure, D, however, shows no evidence of the claimed systematic dependence on reference-list length. More fundamentally, even after controlling for reference-list length, larger teams remain more likely to produce strongly developmental work. Our findings reaffir

ALT Large Language Models (LLMs) such as ChatGPT can estimate research quality from article titles and abstracts, and decision makers may increasingly use LLM scores to support research assessment. In this context, it is a concern that LLM scores may be influenced by linguistic signals rather than the quality of the research described. This study investigates whether explicit novelty claims in research abstracts are associated with higher ChatGPT scores. Using 99,245 journal articles submitted to the UK Research Excellence Framework (REF) 2021, ChatGPT scores and percentile rank gains relative to REF departmental average scores (as a proxy for article-level scores) were compared between abstracts with and without any of 282 three-word phrases identified as indicating a novelty claim. Abstracts containing novelty claim phrases had higher mean ChatGPT quality scores in all four main REF research groupings (panels). Moreover, the score difference was higher for the originality dimension of re

ALT The increasing specialization of scientific research calls for fine-grained representations of publication content that can be applied at scale. SCALE extends the OpenAlex taxonomy with approximately 114,000 Scientific Concepts derived from author keywords, but assigning these Concepts to individual publications remains challenging. We introduce MATCH (Multi-stage Article Tagging to Concept Hierarchies), a framework that assigns SCALE Concepts using article titles, abstracts, and author keywords. MATCH combines multi-view embedding retrieval, deterministic taxonomy-aware filtering, and language-model reranking to select relevant Concepts while preserving their hierarchical paths. Its modular design supports both API-based and self-hosted inference. In a blind evaluation involving 22 domain experts across 11 fields and 110 articles, the self-hosted and API-based configurations achieved Precision of 81.5% and 83.3%, respectively, and Precision@5 of approximately 89% for both, with no sta

ALT Scientific document embeddings such as SPECTER2 are optimized for retrieval over large, heterogeneous corpora. On a small specialized corpus their similarity distributions can become highly compressed, which limits their usefulness for exploratory literature mapping. We study whether weakly supervised in-domain adaptation, using only metadata already available in open citation databases, changes this geometry. On ArtCon, a curated corpus of 704 publications on artificial and machine consciousness, we adapt the SPECTER2 proximity model with a lightweight LoRA adapter trained on positive pairs derived from bibliographic coupling in OpenAlex, using no manually annotated similarity labels. We compare the adapted embeddings with lexical (TF-IDF), citation-based (bibliographic coupling) and generic semantic (SPECTER2) representations under thresholded, fixed-density and symmetric k-nearest-neighbour graph constructions. Adaptation substantially decompresses the similarity distribution (mean

ALT This short conceptual paper recalls to the attention of the scientometrics community some long-standing open methodological problems and proposes new directions that might be taken in renewed engagements with them. These problems are the differential dependency of the global reference score on the included units' observed scores and the dilution of impact measurement accuracy due to collaboration, which causes a global convergence in country citation impact due to international collaborative research. Advantages and disadvantages of existing and new solutions are discussed.

ALT Two documents can be close because they cite the same works or because they write about the same things, and the two kinds of proximity often disagree. Science mapping reads them in separate maps or merges them, as hybrid clustering does; either way the disagreement is lost. We introduce multiplex coupling, a framework that couples the same documents by their references and by their terms, keeps the two layers separate and analyses their relation. Similarities are standardised against a null model, and the agreement between the layers is measured on random pairs of documents. Consensus clustering partitions each layer into roots, groups sharing an intellectual base, and themes, groups sharing a topic. A test on the roots × themes table identifies branching roots and convergent themes, and pairs of roots are followed over time. Every structure is reported with its persistence across numbers of neighbours, which on synthetic data identifies most false branching. Applications to Web of Sc

ALT Digital libraries and knowledge graphs need a way to organize very large collections so that related works are close and any work is quick to reach. The classifications libraries inherited for this, such as LCC and DDC, are maintained by editorial staff, are partly closed, and were not built against a stated, measurable objective. We propose the opposite: a general classification that is fitted, under the constraints that make a classification usable on a shelf or in a browse list, to the same quantity that a companion survey and benchmark (Awad, 2026) ranks existing schemes by. The objective is J = D + 0.005 N, where D is the normalized Dasgupta cost of the class tree on a fixed similarity graph (Dasgupta, 2016) and N is an expected navigation cost in bits with a per-level charge. Two constraints are hard: every class is a contiguous run of one fixed shelf order (the optimal-alphabetic-tree constraint of Hu and Tucker, 1971), and new works are filed by splitting leaf classes only, so

ALT Paper-to-patent citations connect scientific and technological documents, but their interpretability depends on how patents are used in the citing text. This study examines citation contexts and paper-level outcome associations in AI-related articles in PubMed Central (PMC). Screening 189,073 articles identified 746 with explicitly tagged patent references (0.395%), including 713 with 1,544 identifiable in-text patent-citation instances. Three large language models classified these instances by represented content, motivation, textual position, and evaluative stance. Regression models examined associations among these dimensions, and propensity-score-matched regressions compared patent-citing papers with XML-screened controls. Background-oriented citations were predominantly neutral and introductory, whereas adoption-oriented citations were more closely associated with methodological positions and supportive evaluations. These patterns suggest heuristic configurations of contextualizat

ALT Research evaluation has developed around quantity, citation impact and, more recently, the accumulation of research excellence, but these dimensions remain largely dependent on citations, which describe how research is received rather than what it contains. We propose uniqueness as a complementary dimension and measure it with the Research Uniqueness Index (RUI), which asks how far a university's research sits from what is typical in its field, using no citations but the content of the research itself. Each researcher is represented by an embedding of their abstracts, and an unbalanced version of Optimal Transport returns the share of that work which cannot be matched into a random sample of same-field researchers, the standard population; that unmatched share is the score. Because every researcher is scored, a university is a distribution rather than one number, and we treat that distribution as the indicator: it shows whether uniqueness is spread across an institution or concentrated

ALT Science maps describe relationships between papers. Text embedding models build these relationships by turning a paper's text into a list of numbers that gives it a position. Would another model give the same picture? I compare ten models using titles and abstracts of 500,000 publications from the OpenAlex bibliographic catalogue, published in 2000-2024 across 26 disciplines, including Medicine and Physics. Representing each discipline by its papers' average position produces similar arrangements across models, including when other papers are selected. Yet within a discipline and period, two models share, on average, only eight of the 25 papers closest to a given paper. Supplying titles alone also changes much of that list, with effects that vary between models. Models can also disagree about which discipline's papers are more spread out. One measure of spread compares angles between lines drawn from a common point to each paper's position. Moving that point can reverse which disciplin

ALT Artificial intelligence (AI) is being applied to an expanding range of scientific activities, including literature analysis, hypothesis generation, software development and automated experimentation. Yet these capabilities rely on a more fundamental condition: scientific information must remain identifiable and traceable across systems and over time. In practice, different versions of a work and their associated records and files are often scattered across services and local directories. This fragmentation forces researchers to identify and contextualize the same work repeatedly, disrupting continuity from discovery to interpretation and reuse. Here we present Gewu Insight, a research information infrastructure that addresses this discontinuity by organizing scientific works as persistent, stateful research objects. It unifies venue-, topic- and author-based discovery records through a shared paper identity. Within a local library, metadata, versioned resources and research context can

ALT Digital libraries increasingly host scientific publications whose figures encode primary research findings, yet making this visual content searchable, accessible, and reusable remains an open challenge. Large language and multimodal models (LLMs/LMMs) provide the possibility to automate many figure parsing tasks such as determining data trends or extracting data from plots. In this work, we consider if these models can reliably support task-oriented visual data analysis when presented with synthetic, but realistic, scientific figures that contain multiple charts. Our Visual Question Answering (VQA) process scaffolds our tasks along a four-level description model from basic conceptual answers to interpretive and analytical understanding. Additionally, we outline our process for synthetically generating our single and multi-panel (charts in which there are multiple panels present) plots and testing the data with various LMMs using quantitative analysis. In general, we find LMMs become le

ALT 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 A

ALT Author Name Disambiguation systems are commonly evaluated using pairwise and B³ metrics, which summarize clustering quality through aggregate precision and recall but do not explicitly distinguish between lumping (merging publications from different authors) and splitting (fragmenting the publications of a single author). Because these errors can have asymmetric consequences in bibliographic applications, we propose a precision-driven evaluation framework that decomposes clustering quality into complementary instance-level and cluster-level signals. The framework introduces cluster-level Lumping Error and Splitting Error to separately quantify contamination and fragmentation, combines them with standard pairwise and B³ metrics through a Composite Score, and uses the resulting measures for model selection. We characterize the diagnostic behavior of the framework through a controlled synthetic case study and assess the sensitivity of the Composite Score to its weighting scheme. The resul

ALT Selective databases such as Scopus periodically re-evaluate the titles they index and remove those that no longer meet their quality criteria. Once a journal is removed, its subsequent output is no longer recorded, and the consequences of this process have therefore received little attention. This study analyzes the publication output and citation impact of 514 sources delisted from Scopus between 2018 and 2024, based on 678,162 documents retrieved from OpenAlex over a window of three years before and after delisting. Differences between periods are tested with the Wilcoxon signed-rank test for paired samples, globally and by geographic region, field of knowledge and SJR quartile. Output grows until the year of delisting and falls sharply afterwards. The median annual output per journal drops from 35 to 22 documents (-37.1%; p < 0.001; r = 0.30), 61.7% of journals publish less, and 84 (16.3%) have no documents recorded in OpenAlex after delisting. Citations per document, measured over