Citation indices are tools used by the academic community for research and research evaluation which aggregate scientific literature output and measure impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they fail to communicate contextual information about a citation. The usage of citations in research evaluation without consideration of context can be problematic, because a citation that presents contrasting evidence to a paper is treated the same as a citation that presents supporting evidence. To solve this problem, we have used machine learning, traditional document ingestion methods, and a network of researchers to develop a “smart citation index” called scite, which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper and a classification from our deep learning model that indicates whether the statement provides supporting or contrasting evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 25 million full-text scientific articles and currently has a database of more than 880 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation. Peer Review https://publons.com/publon/10.1162/qss_a_00146

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Citation indices are tools used by the academic community for research and research evaluation which aggregate scientific literature output and measure scientific impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they only display paper titles, authors, and the date of publications, and fail to communicate contextual information about why a citation was made. The usage of citations in research evaluation without due consideration to context can be problematic, if only because a citation that disputes a paper is treated the same as a citation that supports it. To solve this problem, we have used machine learning and other techniques to develop a "smart citation index" called scite, which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper, and a classification from our deep learning model that indicates whether the statement provides supporting or disputing evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 23 million full-text scientific articles and currently has a database of more than 800 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation.

Key points While the importance of citation context has long been recognized, simple citation counts remain as a crude measure of importance. Providing citation context should support the publication of careful science instead of headline‐grabbing and salami‐sliced non‐replicable studies. Machine learning has enabled the extraction of citation context for the first time, and made the classification of citation types at scale possible.

Machine learning models allow us to compare languages by showing how hard a task in each language might be to learn and perform well on. Following this line of investigation, we explore what makes a language “hard to pronounce” by modelling the task of grapheme-to-phoneme (g2p) transliteration. By training a character-level transformer model on this task across 22 languages and measuring the model’s proficiency against its grapheme and phoneme inventories, we show that certain characteristics emerge that separate easier and harder languages with respect to learning to pronounce. Namely the complexity of a language's pronunciation from its orthography is due to the expressive or simplicity of its grapheme-tophoneme mapping. Further discussion illustrates how future studies should consider relative data sparsity per language to design fairer cross-lingual comparison tasks.

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Computer programming is increasingly being discussed as a practice within librarianship. However, contemporary discussions about the role of code within librarianship often suggest that librarians should or should not learn code while failing to qualify how and why librarians are employing code in a professional capacity rather than IT professionals. By investigating case studies that describe librarians writing code, this paper qualifies popular discussions of code and librarianship with how and why programming is being used in practice by librarians. While these case studies reveal that programming solutions were developed in the context of lack of IT staff, librarians are not taking over roles or projects traditionally afforded to software and web developers, instead librarians are writing code for data processing and web services as extensions of their normal responsibilities. Further discussion explores software engineering as the primary concern of librarians who code professionally.