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1. Writing a massively multi‐authored paper: Overcoming barriers to meaningful authorship for all

   https://doi.org/10.1111/2041-210X.14096  

   Borer, Elizabeth T.; MacDougall, Andrew S.; Stevens, Carly J.; Sullivan, Lauren L.; Wilfahrt, Peter A.; Seabloom, Eric W. (April 2023, Methods in Ecology and Evolution)

   Abstract The value of large‐scale collaborations for solving complex problems is widely recognized, but many barriers hinder meaningful authorship for all on the resulting multi‐author publications. Because many professional benefits arise from authorship, much of the literature on this topic has focused on cheating, conflict and effort documentation. However, approaches specifically recognizing and creatively overcoming barriers to meaningful authorship have received little attention.We have developed an inclusive authorship approach arising from 15 years of experience coordinating the publication of over 100 papers arising from a long‐term, international collaboration of hundreds of scientists.This method of sharing a paper initially as a storyboard with clear expectations, assignments and deadlines fosters communication and creates unambiguous opportunities for all authors to contribute intellectually. By documenting contributions through this multi‐step process, this approach ensures meaningful engagement by each author listed on a publication.The perception that co‐authors on large authorship publications have not meaningfully contributed underlies widespread institutional bias against multi‐authored papers, disincentivizing large collaborations despite their widely recognized value for advancing knowledge. Our approach identifies and overcomes key barriers to meaningful contributions, protecting the value of authorship even on massively multi‐authored publications. 

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2. Co-author network analysis of human-centered design for development

   https://doi.org/10.1017/dsj.2018.1  

   Li, Nancy; Kramer, Julia; Gordon, Pierce; Agogino, Alice (January 2018, Design Science)

   Human-centered design (HCD) offers a systematic approach to innovation practice, driven by customer research and feedback throughout the design process. Within the community of engineers and researchers who engage in design for global development, interest in HCD has grown in the past decade. In this paper, we examine the human-centered design for development (HCD+D) academic community to better understand the interactions between researchers. By building and evaluating a co-authorship network from a dataset of HCD+D papers, in which the nodes are researchers and the connecting links are co-authorship relationships, we provide a decade-long benchmark to answer a variety of questions about collaboration patterns within this emerging field. Our analysis shows that most HCD+D authors publish few papers and are part of small, well-connected sub-communities. Influential authors that bridge separate communities are few. HCD+D is emerging from disparate disciplines and widely shared scholarship across disciplines continues to be developed. Influential authors in HCD+D play a large role in shaping HCD+D, yet there are few authors that are in a position to connect and influence collaborative research. Our analysis gives rise to several implications including an increased need for cross-disciplinary collaboration and the need for a stronger core of HCD+D practitioners. 

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3. Co-Authorship Maps to Support Leadership Selection

   https://doi.org/10.13023/VG16-XQ81  

   Chen, Robert HC (April 2024, University of Kentucky Libraries)

   VOSViewer co-authorship mapping is a powerful tool typically used for analyzing research collaboration. Users provide publication data and VOSViewer produces a map where authors are plotted on a 2-dimensional map based on how often they are in the author lists of the same publication. In this presentation, I propose a series of tweaks to the input data that can leverage co-authorship maps to support leadership selection based on how often candidates co-author papers with their institutional peers and some of the attributes of these papers. I will suggest how best to interpret the resulting maps and address the major assumptions that must be kept in mind when using these maps for this purpose. Lastly, I will discuss the lessons learned when we offered such maps to support a series of internal leadership selections for Canada’s largest research hospital. Presented at the 2024 Research Analytics Summit in Albuquerque, NM 

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4. A Call for Clarity in Contemporary Authorship Attribution Evaluation

   https://doi.org/10.26615/978-954-452-072-4_132  

   Riddell, Allen; Wang, Haining; Juola, Patrick (January 2021, Proceedings of the International Conference on Recent Advances in Natural Language Processing)

   Recent research has documented that results reported in frequently-cited authorship attribution papers are difficult to reproduce. Inaccessible code and data are often proposed as factors which block successful reproductions. Even when original materials are available, problems remain which prevent researchers from comparing the effectiveness of different methods. To solve the remaining problems—the lack of fixed test sets and the use of inappropriately homogeneous corpora—our paper contributes materials for five closed-set authorship identification experiments. The five experiments feature texts from 106 distinct authors. Experiments involve a range of contemporary non-fiction American English prose. These experiments provide the foundation for comparable and reproducible authorship attribution research involving contemporary writing. 

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5. Adjectives and adverbs as stylometric analysis parameters

   https://doi.org/10.1007/s42803-023-00065-y  

   Lukin, Eugenia; Roberts, James Cooper; Berdik, David; Mugar, Eliana; Juola, Patrick (November 2023, International Journal of Digital Humanities)

   Abstract The present study considers the role of adjectives and adverbs in stylometric analysis and authorship attribution. Adjectives and adverbs allow both for variations in placement and order (adverbs) and variations in type (adjectives). This preliminary study examines a collection of 25 English-language blogs taken from the Schler Blog corpus, and the Project Gutenberg corpus with specific emphasis on 3 works. Within the blog corpora, the first and last 100 lines were extracted for the purpose of analysis. Project Gutenberg corpora were used in full. All texts were processed and part-of-speech tagged using the Python NLTK package. All adverbs were classified as sentence-initial, preverbal, interverbal, postverbal, sentence-final, or none-of-the-above. The adjectives were classified into types according to the universal English type hierarchy (Cambridge Dictionary Online, 2021; Annear, 1964) manually by one of the authors. Ambiguous adjectives were classified according to their context. For the adverbs, the initial samples were paired and used as training data to attribute the final samples. This resulted in 600 trials under each of five experimental conditions. We were able to attribute authorship with an average accuracy of 9.7% greater than chance across all five conditions. Confirmatory experiments are ongoing with a larger sample of English-language blogs. This strongly suggests that adverbial placement is a useful and novel idiolectal variable for authorship attribution (Juola et al., 2021). For the adjective, differences were found in the type of adjective used by each author. Percent use of each type varied based upon individual preference and subject-matter (e.g. Moby Dick had a large number of adjectives related to size and color). While adverbial order and placement are highly variable, adjectives are subject to rigid restrictions that are not violated across texts and authors. Stylometric differences in adjective use generally involve the type and category of adjectives preferred by the author. Future investigation will focus, likewise, on whether adverbial variation is similarly analyzable by type and category of adverb. 

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