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1. Nurses, Managers, and Engineers – Oh My! Disciplinary Perceptions of Intuition and Its Role in Expertise Development

   https://doi.org/10.1109/FIE44824.2020.9274026  

   Aaron, Caitlyn; Miskioglu, Elif; Martin, Kaela M.; Shannon, Brooke; Carberry, Adam (October 2020, 2020 IEEE Frontiers in Education Conference (FIE))

   null (Ed.)

   This Research Category - Full Paper presents initial emergent themes from our quest to understand the construct of intuition. Our work uses theories of expertise development and dual-cognitive processing frameworks to provide a theoretical grounding to define discipline-specific intuition. We hypothesize that intuition can be observed in disciplinary experts through discussions of experience and decision-making processes. Interviews were conducted with professionals in three fields - engineering, nursing, and business management - that engage intuition in decision-making. A comparative analysis of emergent themes is presented to understand similarities and differences in use and definition across these disciplines. Parallel grounded theory and critical incident technique approaches were used to identify perceptions and incidents of intuition. Results suggest that intuition can be defined as a "sense of knowing" that is context specific and at least partly attributable to experience. Inclusion of multiple fields and comparisons across disciplines form the foundation for our future work focusing solely on engineering intuition. 

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2. The collective method: collaborative social science research and scholarly accountability

   https://doi.org/10.1177/1468794117743461  

   Pardee, Jessica W; Fothergill, Alice; Weber, Lynn; Peek, Lori (December 2018, Qualitative Research)

   null (Ed.)

   This article conceptualizes the collective method to describe how 12 scholars worked collaboratively to study the effects of displacement following Hurricane Katrina. The collective method is defined as an integrated, reflexive process of research design and implementation in which a diverse group of scholars studying a common phenomenon-yet working on independent projects-engage in repeated theoretical and methodological discussions to improve (1) research transparency and accountability and (2) the rigor and efficacy of each member’s unique project. This process generates critical discussions over researchers’ and respondents’ positionality, the framework of intersectionality, and applied ethics. Informed by feminist theoretical and methodological considerations of reflexivity, insider-outsider positionality, power relations, and social justice, the collective method can enhance scholars’ standpoints regarding philosophical, ethical, and strategic issues that emerge in the research process. 

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3. Identifying the Central Figure of a Scientific Paper

   https://doi.org/10.1109/ICDAR.2019.00173  

   Yang, Sean T.; Lee, Po-Shen; Kazakova, Lia; Joshi, Abhishek; Oh, Bum Mook; West, Jevin D.; Howe, Bill (September 2019, 2019 International Conference on Document Analysis and Recognition (ICDAR))

   Publishers are increasingly using graphical abstracts to facilitate scientific search, especially across disciplinary boundaries. They are presented on various media, easily shared and information rich. However, very small amount of scientific publications are equipped with graphical abstracts. What can we do with the vast majority of papers with no selected graphical abstract? In this paper, we first hypothesize that scientific papers actually include a "central figure" that serve as a graphical abstract. These figures convey the key results and provide a visual identity for the paper. Using survey data collected from 6,263 authors regarding 8,353 papers over 15 years, we find that over 87% of papers are considered to contain a central figure, and that these central figures are primarily used to summarize important results, explain the key methods, or provide additional discussion. We then train a model to automatically recognize the central figure, achieving top-3 accuracy of 78% and exact match accuracy of 34%. We find that the primary boost in accuracy comes from figure captions that resemble the abstract. We make all our data and results publicly available at https://github.com/viziometrics/centraul_figure. Our goal is to automate central figure identification to improve search engine performance and to help scientists connect ideas across the literature. 

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4. What did they just say? Building a Rosetta stone for geoscience and machine learning

   Mordensky, S.P.; Lipor, J.; Burns, E; Lindsey, C.R. (August 2022, 2022 Geothermal Rising Conference)

   Modern advancements in science and engineering are built upon multidisciplinary projects that bring experts together from different fields. Within their respective disciplines, researchers rely on precise terminology for specific ideas, principles, methods, and theories. Hence, the potential for miscommunication is substantial, especially when common words have been adopted by one (or both) group(s) to represent very specific, precise, but, perhaps, different concepts. Under the best circumstances, misunderstanding key terms will lead toward a breakdown of efficiency. Under less optimal conditions, miscommunication will sow frustration, lead to errors, and inhibit scientific breakthroughs. Here, our research group of geoscientists and machine learning experts presents a process to help geoscientists understand the fundamentals of supervised learning by describing the general workflow (i.e., a conceptual pipeline) for supervised learning that must be understood by all the parties involved in a geoscience-machine learning endeavor. Terms critical for machine learning are introduced, defined, and used within the context of an overly simplified mock hydrological study to illustrate their appropriate usage, and then used again in the context of a published geothermal-machine learning study. These key terms are divided into two groups, which are 1) essential to the field of machine learning but are predominantly absent in geoscience or 2) homonyms (i.e., words with the same spelling or pronunciation but with different meanings) between the fields. Lastly, we discuss a few other important homonyms that were not introduced in the general workflow but arise regularly in machine learning applications. 

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5. What Did They Just Say? Building a Rosetta Stone for Geoscience and Machine Learning

   Mordensky, S.P.; Lipor, J.; Burns, E; Lindsey, C.R. (August 2022, 2022 Geothermal Rising Conference)

   Modern advancements in science and engineering are built upon multidisciplinary projects that bring experts together from different fields. Within their respective disciplines, researchers rely on precise terminology for specific ideas, principles, methods, and theories. Hence, the potential for miscommunication is substantial, especially when common words have been adopted by one (or both) group(s) to represent very specific, precise, but, perhaps, different concepts. Under the best circumstances, misunderstanding key terms will lead toward a breakdown of efficiency. Under less optimal conditions, miscommunication will sow frustration, lead to errors, and inhibit scientific breakthroughs. Here, our research group of geoscientists and machine learning experts presents a process to help geoscientists understand the fundamentals of supervised learning by describing the general workflow (i.e., a conceptual pipeline) for supervised learning that must be understood by all the parties involved in a geoscience-machine learning endeavor. Terms critical for machine learning are introduced, defined, and used within the context of an overly simplified mock hydrological study to illustrate their appropriate usage, and then used again in the context of a published geothermal-machine learning study. These key terms are divided into two groups, which are 1) essential to the field of machine learning but are predominantly absent in geoscience or 2) homonyms (i.e., words with the same spelling or pronunciation but with different meanings) between the fields. Lastly, we discuss a few other important homonyms that were not introduced in the general workflow but arise regularly in machine learning applications. 

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