Results for 'Data-driven originalism'

994 found
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  1. Reconceptualizing American Democracy: The First Principles.Angelina Inesia-Forde - 2023 - Asian Journal of Basic Science and Research 5 (4):01-47.
    An outstanding group of leaders left evidence that a richer and more sustainable democracy could be achieved with American independence and democratic principles integrated into a new republican form of government. They were moved by principles that are the very spirit of democracy. These principles are needed to enhance democracy and improve well-being. Using the constructivist tradition of grounded theory and Aristotle’s conception of abstraction, the article proposes a theory of the first principles of democracy based on substantive data: (...)
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  2.  83
    Knowledge-driven versus data-driven logics.Didier Dubois, Petr Hájek & Henri Prade - 2000 - Journal of Logic, Language and Information 9 (1):65--89.
    The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data - driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by (...)
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  3.  14
    A Data-Driven Approach to Optimizing Medical-Legal Partnership Performance and Joint Advocacy.Andrew F. Beck, Adrienne W. Henize, Melissa D. Klein, Alexandra M. S. Corley, Elaine E. Fink & Robert S. Kahn - 2023 - Journal of Law, Medicine and Ethics 51 (4):880-888.
    Medical-legal partnerships connect legal advocates to healthcare providers and settings. Maintaining effectiveness of medical-legal partnerships and consistently identifying opportunities for innovation and adaptation takes intentionality and effort. In this paper, we discuss ways in which our use of data and quality improvement methods have facilitated advocacy at both patient (client) and population levels as we collectively pursue better, more equitable outcomes.
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  4.  31
    A data-driven computational semiotics: The semantic vector space of Magritte’s artworks.Jean-François Chartier, Davide Pulizzotto, Louis Chartrand & Jean-Guy Meunier - 2019 - Semiotica 2019 (230):19-69.
    The rise of big digital data is changing the framework within which linguists, sociologists, anthropologists, and other researchers are working. Semiotics is not spared by this paradigm shift. A data-driven computational semiotics is the study with an intensive use of computational methods of patterns in human-created contents related to semiotic phenomena. One of the most promising frameworks in this research program is the Semantic Vector Space (SVS) models and their methods. The objective of this article is to (...)
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  5.  58
    Understanding climate phenomena with data-driven models.Benedikt Knüsel & Christoph Baumberger - 2020 - Studies in History and Philosophy of Science Part A 84 (C):46-56.
    In climate science, climate models are one of the main tools for understanding phenomena. Here, we develop a framework to assess the fitness of a climate model for providing understanding. The framework is based on three dimensions: representational accuracy, representational depth, and graspability. We show that this framework does justice to the intuition that classical process-based climate models give understanding of phenomena. While simple climate models are characterized by a larger graspability, state-of-the-art models have a higher representational accuracy and representational (...)
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  6.  36
    Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms.Tianjiao Chu, David Danks & Clark Glymour - unknown
    Tianjaou Chu, David Danks, and Clark Glymour. Data Driven Methods for Nonlinear Granger Causality: Climate Teleconnection Mechanisms.
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  7.  23
    Data-Driven Superheating Control of Organic Rankine Cycle Processes.Jianhua Zhang, Xiao Tian, Zhengmao Zhu & Mifeng Ren - 2018 - Complexity 2018:1-8.
    In this paper, a data-driven superheating control strategy is developed for organic Rankine cycle processes. Due to non-Gaussian stochastic disturbances imposed on heat sources, the quantized minimum error entropy is adopted to construct the performance index of superheating control systems. Furthermore, particle swarm optimization algorithm is applied to obtain optimal control law by minimizing the performance index. The implementation procedures of the presented superheating control system in an ORC-based waste heat recovery process are presented. The simulation results testify (...)
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  8.  27
    Descriptive multiscale modeling in data-driven neuroscience.Philipp Haueis - 2022 - Synthese 200 (2):1-26.
    Multiscale modeling techniques have attracted increasing attention by philosophers of science, but the resulting discussions have almost exclusively focused on issues surrounding explanation (e.g., reduction and emergence). In this paper, I argue that besides explanation, multiscale techniques can serve important exploratory functions when scientists model systems whose organization at different scales is ill-understood. My account distinguishes explanatory and descriptive multiscale modeling based on which epistemic goal scientists aim to achieve when using multiscale techniques. In explanatory multiscale modeling, scientists use multiscale (...)
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  9.  6
    Data-Driven Robust Optimization of the Vehicle Routing Problem with Uncertain Customers.Jingling Zhang, Yusu Sun, Qinbing Feng, Yanwei Zhao & Zheng Wang - 2022 - Complexity 2022:1-15.
    With the increasing proportion of the logistics industry in the economy, the study of the vehicle routing problem has practical significance for economic development. Based on the vehicle routing problem, the customer presence probability data are introduced as an uncertain random parameter, and the VRP model of uncertain customers is established. By optimizing the robust uncertainty model, combined with a data-driven kernel density estimation method, the distribution feature set of historical data samples can then be fitted, (...)
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  10.  18
    Data-driven sciences: From wonder cabinets to electronic databases.Bruno J. Strasser - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):85-87.
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  11.  81
    Data-driven sciences: From wonder cabinets to electronic databases.Bruno J. Strasser - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):85-87.
  12.  18
    Data-Driven Decision Making and Dewey's Science of Education.Natalie Schelling & Lance E. Mason - 2021 - Education and Culture 37 (1):41-59.
  13.  13
    Data-driven learning and academic oral discourse.Thi Thu Hoai Masset-Martin Tran - 2023 - Corpus 24 (24).
    Dans le cadre de ce travail, nous présentons une expérimentation menée auprès d’un public allophone inscrit à une formation universitaire. Ce travail a pour objectif de relever, d’une part, les spécificités dans les productions orales de ce public, et d’autre part, de démontrer l’intérêt d’un apprentissage sur corpus afin de construire un exposé structuré. Cette étude permet de s’ouvrir à d’autres perspectives didactiques en partant d’un corpus d’apprenants.
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  14.  9
    Data-Driven Finite Element Models of Passive Filamentary Networks.Brian Adam & Sorin Mitran - 2018 - Complexity 2018:1-7.
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  15.  34
    Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process.Zhengsong Wang, Dakuo He, Xu Zhu, Jiahuan Luo, Yu Liang & Xu Wang - 2017 - Complexity:1-17.
    A novel data-driven model-free adaptive control approach is first proposed by combining the advantages of model-free adaptive control and data-driven optimal iterative learning control, and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed (...)
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  16. Data driven methods for Granger causality and contemporaneous causality with non-linear corrections: Climate teleconnection mechanisms.Clark Glymour - unknown
    We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the (...)
     
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  17.  40
    Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models.Indrė Žliobaitė & Bart Custers - 2016 - Artificial Intelligence and Law 24 (2):183-201.
    Increasing numbers of decisions about everyday life are made using algorithms. By algorithms we mean predictive models (decision rules) captured from historical data using data mining. Such models often decide prices we pay, select ads we see and news we read online, match job descriptions and candidate CVs, decide who gets a loan, who goes through an extra airport security check, or who gets released on parole. Yet growing evidence suggests that decision making by algorithms may discriminate people, (...)
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  18.  8
    Data-driven campaigns in public sensemaking: Discursive positions, contextualization, and maneuvers in American, British, and German debates around computational politics.Lena Fölsche & Christian Pentzold - 2020 - Communications 45 (s1):535-559.
    Our article examines how journalistic reports and online comments have made sense of computational politics. It treats the discourse around data-driven campaigns as its object of analysis and codifies four main perspectives that have structured the debates about the use of large data sets and data analytics in elections. We study American, British, and German sources on the 2016 United States presidential election, the 2017 United Kingdom general election, and the 2017 German federal election. There, groups (...)
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  19.  4
    A Data-Driven Expectation Prediction Framework Based on Social Exchange Theory.Enguo Cao, Jinzhi Jiang, Yanjun Duan & Hui Peng - 2022 - Frontiers in Psychology 12.
    Along with the rapid application of new information technologies, the data-driven era is coming, and online consumption platforms are booming. However, massive user data have not been fully developed for design value, and the application of data-driven methods of requirement engineering needs to be further expanded. This study proposes a data-driven expectation prediction framework based on social exchange theory, which analyzes user expectations in the consumption process, and predicts improvement plans to assist designers (...)
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  20. Data Driven Methods for Granger Causality and Contemporaneous Causality with Non-Linear Corrections: Climate Teleconnection Mechanisms.T. Chu & D. Danks - unknown
    We describe a unification of old and recent ideas for formulating graphical models to explain time series data, including Granger causality, semi-automated search procedures for graphical causal models, modeling of contemporaneous influences in times series, and heuristic generalized additive model corrections to linear models. We illustrate the procedures by finding a structure of exogenous variables and mediating variables among time series of remote geospatial indices of ocean surface temperatures and pressures. The analysis agrees with known exogenous drivers of the (...)
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  21. Five Ethical Challenges for Data-Driven Policing.Jeremy Davis, Duncan Purves, Juan Gilbert & Schuyler Sturm - 2022 - AI and Ethics 2:185-198.
    This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” emcompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated (...)
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  22.  11
    Data-Driven Technology in Event-Based Vision.Ruolin Sun, Dianxi Shi, Yongjun Zhang, Ruihao Li & Ruoxiang Li - 2021 - Complexity 2021:1-19.
    Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated (...)
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  23.  32
    DataDriven Discovery of Physical Laws.Pat Langley - 1981 - Cognitive Science 5 (1):31-54.
    BACON.3 is a production system that discovers empirical laws. Although it does not attempt to model the human discovery process in detail, it incorporates some general heuristics that can lead to discovery in a number of domains. The main heuristics detect constancies and trends in data, and lead to the formulation of hypotheses and the definition of theoretical terms. Rather than making a hard distinction between data and hypotheses, the program represents information at varying levels of description. The (...)
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  24.  9
    Data-Driven Method for Passenger Path Choice Inference in Congested Subway Network.Guanghui Su, Bingfeng Si, Fang Zhao & He Li - 2022 - Complexity 2022:1-13.
    In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path (...)
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  25.  3
    Data ratcheting and data-driven organisational change in transport.Liam Heaphy - 2019 - Big Data and Society 6 (2).
    This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, conservative ethos, (...)
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  26.  19
    Data-Driven Dialogue Models: Applying Formal and Computational Tools to the Study of Financial And Moral Dialogues.Olena Yaskorska-Shah - 2020 - Studies in Logic, Grammar and Rhetoric 63 (1):185-208.
    This paper proposes two formal models for understanding real-life dialogues, aimed at capturing argumentative structures performatively enacted during conversations. In the course of the investigation, two types of discourse with a high degree of well-structured argumentation were chosen: moral debate and financial communication. The research project found itself confronted by a need to analyse, structure and formally describe large volumes of textual data, where this called for the application of computational tools. It is expected that the results of the (...)
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  27.  28
    Data driven Markov Chain Monte Carlo algorithm.Alan Yuille & Daniel Kersten - 2006 - Trends in Cognitive Sciences 10 (7):301-308.
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  28.  8
    Data-Driven Research on the Matching Degree of Eyes, Eyebrows and Face Shapes.Jian Zhao, Meng Zhang, Chen He & Kainan Zuo - 2019 - Frontiers in Psychology 10.
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  29.  53
    Data-Driven Hybrid Internal Temperature Estimation Approach for Battery Thermal Management.Kailong Liu, Kang Li, Qiao Peng, Yuanjun Guo & Li Zhang - 2018 - Complexity 2018:1-15.
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  30.  9
    Data-driven approaches to empirical discovery.Pat Langley & Jan M. Zytkow - 1989 - Artificial Intelligence 40 (1-3):283-312.
  31.  13
    Research on Chinese Consumers’ Attitudes Analysis of Big-Data Driven Price Discrimination Based on Machine Learning.Jun Wang, Tao Shu, Wenjin Zhao & Jixian Zhou - 2022 - Frontiers in Psychology 12:803212.
    From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers’ attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires published (...)
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  32.  18
    A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.Arnau Dillen, Elke Lathouwers, Aleksandar Miladinović, Uros Marusic, Fakhredinne Ghaffari, Olivier Romain, Romain Meeusen & Kevin De Pauw - 2022 - Frontiers in Human Neuroscience 16.
    Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this (...)
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  33.  14
    A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics.Arnau Dillen, Elke Lathouwers, Aleksandar Miladinović, Uros Marusic, Fakhreddine Ghaffari, Olivier Romain, Romain Meeusen & Kevin De Pauw - 2022 - Frontiers in Human Neuroscience 16.
    Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram signals to improve the control of active prostheses with brain-computer interfaces. Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our knowledge of the neural patterns associated with lower limb movement. Therefore, the main objective of this (...)
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  34.  13
    A Data-Driven Parameter Adaptive Clustering Algorithm Based on Density Peak.Tao Du, Shouning Qu & Qin Wang - 2018 - Complexity 2018:1-14.
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  35.  14
    Perils of data-driven equity: Safety-net care and big data’s elusive grasp on health inequality.Taylor M. Cruz - 2020 - Big Data and Society 7 (1).
    Large-scale data systems are increasingly envisioned as tools for justice, with big data analytics offering a key opportunity to advance health equity. Health systems face growing public pressure to collect data on patient “social factors,” and advocates and public officials seek to leverage such data sources as a means of system transformation. Despite the promise of this “data-driven” strategy, there is little empirical work that examines big data in action directly within the sites (...)
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  36. Optimization of Scientific Reasoning: a Data-Driven Approach.Vlasta Sikimić - 2019 - Dissertation,
    Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in (...)
     
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  37.  4
    Data-driven research and healthcare: public trust, data governance and the NHS. [REVIEW]Charalampia Kerasidou & Angeliki Kerasidou - 2023 - BMC Medical Ethics 24 (1):1-9.
    It is widely acknowledged that trust plays an important role for the acceptability of data sharing practices in research and healthcare, and for the adoption of new health technologies such as AI. Yet there is reported distrust in this domain. Although in the UK, the NHS is one of the most trusted public institutions, public trust does not appear to accompany its data sharing practices for research and innovation, specifically with the private sector, that have been introduced in (...)
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  38.  27
    Datadriven approaches to information access.Susan Dumais - 2003 - Cognitive Science 27 (3):491-524.
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  39.  10
    Data-Driven Detection of Figurative Language Use in Electronic Language Resources.Wim Peters & Yorick Wilks - 2003 - Metaphor and Symbol 18 (3):161-173.
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  40.  37
    Data-Driven Visual Performance Analysis in Soccer: An Exploratory Prototype.Alejandro Benito Santos, Roberto Theron, Antonio Losada, Jaime E. Sampaio & Carlos Lago-Peñas - 2018 - Frontiers in Psychology 9.
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  41.  10
    Data-driven type checking in open domain question answering.Stefan Schlobach, David Ahn, Maarten de Rijke & Valentin Jijkoun - 2007 - Journal of Applied Logic 5 (1):121-143.
  42.  7
    A data-driven, hyper-realistic method for visualizing individual mental representations of faces.Daniel N. Albohn, Stefan Uddenberg & Alexander Todorov - 2022 - Frontiers in Psychology 13.
    Research in person and face perception has broadly focused on group-level consensus that individuals hold when making judgments of others. However, a growing body of research demonstrates that individual variation is larger than shared, stimulus-level variation for many social trait judgments. Despite this insight, little research to date has focused on building and explaining individual models of face perception. Studies and methodologies that have examined individual models are limited in what visualizations they can reliably produce to either noisy and blurry (...)
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  43.  10
    Analyzing Nonlinear Dynamics via Data-Driven Dynamic Mode Decomposition-Like Methods.Soledad Le Clainche & José M. Vega - 2018 - Complexity 2018:1-21.
    This article presents a review on two methods based on dynamic mode decomposition and its multiple applications, focusing on higher order dynamic mode decomposition and spatiotemporal Koopman decomposition. These methods are purely data-driven, using either numerical or experimental data, and permit reconstructing the given data and identifying the temporal growth rates and frequencies involved in the dynamics and the spatial growth rates and wavenumbers in the case of the spatiotemporal Koopman decomposition. Thus, they may be used (...)
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  44.  14
    A Data-Driven Argument in Bioethics: Why Theologically Grounded Concepts May Not Provide the Necessary Intellectual Resources to Discuss Inequality and Injustice in Healthcare Contexts.Tomasz Żuradzki & Karolina Wiśniowska - 2020 - American Journal of Bioethics 20 (12):25-28.
    In this paper, we use an innovative, empirical, and–as yet–rarely applied method in bioethics, namely corpus analysis, which is commonly used in literature studies (Moretti 2013), linguistics (Bake...
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  45.  17
    Locative media and data-driven computing experiments.Leighton Evans, Rob Kitchin & Sung-Yueh Perng - 2016 - Big Data and Society 3 (1).
    Over the past two decades urban social life has undergone a rapid and pervasive geocoding, becoming mediated, augmented and anticipated by location-sensitive technologies and services that generate and utilise big, personal, locative data. The production of these data has prompted the development of exploratory data-driven computing experiments that seek to find ways to extract value and insight from them. These projects often start from the data, rather than from a question or theory, and try to (...)
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  46.  20
    Sharing whilst caring: solidarity and public trust in a data-driven healthcare system.Ruth Horn & Angeliki Kerasidou - 2020 - BMC Medical Ethics 21 (1):1-7.
    Background In the UK, the solidaristic character of the NHS makes it one of the most trusted public institutions. In recent years, the introduction of data-driven technologies in healthcare has opened up the space for collaborations with private digital companies seeking access to patient data. However, these collaborations appear to challenge the public’s trust in the. Main text In this paper we explore how the opening of the healthcare sector to private digital companies challenges the existing social (...)
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  47.  37
    Just data? Solidarity and justice in data-driven medicine.Matthias Braun & Patrik Hummel - 2020 - Life Sciences, Society and Policy 16 (1):1-18.
    This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between the good and the right, harnessing personal health data towards the development and refinement of data-driven medicine is to be welcomed from the perspective of the good. Enacting solidarity drives progress in research and clinical practice. At the same time, such acts of sharing could—especially considering current developments in big data and artificial intelligence—compromise the (...)
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  48.  16
    ‘It depends on your threat model’: the anticipatory dimensions of resistance to data-driven surveillance.Becky Kazansky - 2021 - Big Data and Society 8 (1).
    While many forms of data-driven surveillance are now a ‘fact’ of contemporary life amidst datafication, obtaining concrete knowledge of how different institutions exploit data presents an ongoing challenge, requiring the expertise and power to untangle increasingly complex and opaque technological and institutional arrangements. The how and why of potential surveillance are thus wrapped in a form of continuously produced uncertainty. How then, do affected groups and individuals determine how to counter the threats and harms of surveillance? Responding (...)
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  49. Introduction: Making sense of data-driven research in the biological and biomedical sciences.S. Leonelli - 2012 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 43 (1):1-3.
  50.  8
    The Emotional Content of Children's Writing: A DataDriven Approach.Yuzhen Dong, Yaling Hsiao, Nicola Dawson, Nilanjana Banerji & Kate Nation - 2024 - Cognitive Science 48 (3):e13423.
    Emotion is closely associated with language, but we know very little about how children express emotion in their own writing. We used a large‐scale, cross‐sectional, and datadriven approach to investigate emotional expression via writing in children of different ages, and whether it varies for boys and girls. We first used a lexicon‐based bag‐of‐words approach to identify emotional content in a large corpus of stories (N>100,000) written by 7‐ to 13‐year‐old children. Generalized Additive Models were then used to model (...)
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