Pub Date : 2024-04-01DOI: 10.1177/26339137241241307
Jason W. Burton, Abdullah Almaatouq, M. Rahimian, Ulrike Hahn
Digitally enabled means for judgment aggregation have renewed interest in “wisdom of the crowd” effects and kick-started collective intelligence design as an emerging field in the cognitive and computational sciences. A keenly debated question here is whether social influence helps or hinders collective accuracy on estimation tasks, with recent results on the role of network structure hinting at a reconciliation of seemingly contradictory past results. Yet, despite a growing body of literature linking social network structure and collective accuracy, strategies for exploiting network structure to harness crowd wisdom are underexplored. We introduce one such strategy: rewiring algorithms that dynamically manipulate the structure of communicating social networks. Through agent-based simulations and an online multiplayer experiment, we provide a proof of concept showing how rewiring algorithms can increase the accuracy of collective estimations—even in the absence of knowledge of the ground truth. However, we also find that the algorithms’ effects are contingent on the distribution of estimates initially held by individuals before communication occurs. •Human-centered computing → Collaborative and social computing• Applied computing → Psychology.
{"title":"Algorithmically mediating communication to enhance collective decision-making in online social networks","authors":"Jason W. Burton, Abdullah Almaatouq, M. Rahimian, Ulrike Hahn","doi":"10.1177/26339137241241307","DOIUrl":"https://doi.org/10.1177/26339137241241307","url":null,"abstract":"Digitally enabled means for judgment aggregation have renewed interest in “wisdom of the crowd” effects and kick-started collective intelligence design as an emerging field in the cognitive and computational sciences. A keenly debated question here is whether social influence helps or hinders collective accuracy on estimation tasks, with recent results on the role of network structure hinting at a reconciliation of seemingly contradictory past results. Yet, despite a growing body of literature linking social network structure and collective accuracy, strategies for exploiting network structure to harness crowd wisdom are underexplored. We introduce one such strategy: rewiring algorithms that dynamically manipulate the structure of communicating social networks. Through agent-based simulations and an online multiplayer experiment, we provide a proof of concept showing how rewiring algorithms can increase the accuracy of collective estimations—even in the absence of knowledge of the ground truth. However, we also find that the algorithms’ effects are contingent on the distribution of estimates initially held by individuals before communication occurs. •Human-centered computing → Collaborative and social computing• Applied computing → Psychology.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"8 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140765405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.1177/26339137241241313
Yi-Chi Liao, George B. Mo, John J. Dudley, Chun-Lien Cheng, Liwei Chan, P. O. Kristensson, Antti Oulasvirta
Designing interaction techniques for end-users often involves exploring vast design spaces while balancing many objectives. Bayesian optimization offers a principled human-in-the-loop method for selecting designs for evaluation to efficiently explore such design spaces. To date, the application of Bayesian optimization in a human-in-the-loop setting has largely been restricted to optimization, or customization, of interaction techniques for individual user needs. In practice, interaction techniques are typically designed for a target population or group of users, with the goal is to produce a design that works well for most users. To accommodate this common use case in interaction technique design, we introduce two practical approaches that facilitate multi-objective Bayesian optimization at the group level. Specifically, our approaches streamline the process of (1) deriving designs suitable for a group of users from data collected in individual user evaluations; and (2) deriving an initialization from group data to improve the efficiency of design optimization for new users. We demonstrate the advantages of these practical approaches in two multi-phase user studies involving the design of non-trivial interaction techniques.
{"title":"Practical approaches to group-level multi-objective Bayesian optimization in interaction technique design","authors":"Yi-Chi Liao, George B. Mo, John J. Dudley, Chun-Lien Cheng, Liwei Chan, P. O. Kristensson, Antti Oulasvirta","doi":"10.1177/26339137241241313","DOIUrl":"https://doi.org/10.1177/26339137241241313","url":null,"abstract":"Designing interaction techniques for end-users often involves exploring vast design spaces while balancing many objectives. Bayesian optimization offers a principled human-in-the-loop method for selecting designs for evaluation to efficiently explore such design spaces. To date, the application of Bayesian optimization in a human-in-the-loop setting has largely been restricted to optimization, or customization, of interaction techniques for individual user needs. In practice, interaction techniques are typically designed for a target population or group of users, with the goal is to produce a design that works well for most users. To accommodate this common use case in interaction technique design, we introduce two practical approaches that facilitate multi-objective Bayesian optimization at the group level. Specifically, our approaches streamline the process of (1) deriving designs suitable for a group of users from data collected in individual user evaluations; and (2) deriving an initialization from group data to improve the efficiency of design optimization for new users. We demonstrate the advantages of these practical approaches in two multi-phase user studies involving the design of non-trivial interaction techniques.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"75 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140522386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1177/26339137231207633
Shubhankar P Patankar, Dale Zhou, Christopher W Lynn, Jason Z Kim, Mathieu Ouellet, Harang Ju, Perry Zurn, David M Lydon-Staley, Dani S Bassett
Theoretical constructs, such as the information gap theory and compression progress theory, seek to explain how humans practice curiosity. According to the former, curiosity is the drive to acquire information missing from our understanding of the world. According to the latter, curiosity is the drive to construct parsimonious mental world models. To complement the densification and simplification processes inherent to these frameworks, we propose the conformational change theory, wherein we posit that curiosity builds mental models with marked conceptual flexibility. We formalize curiosity as a knowledge-network-building process to investigate each theoretical account for individuals and collectives. In knowledge networks, gaps can be identified as topological cavities, compression progress can be quantified using network compressibility, and flexibility can be measured as the number of conformational degrees of freedom. We find that curiosity fills gaps and constructs increasingly compressible and flexible knowledge networks. Across individuals and collectives, we determine the contexts in which each account is explanatory, clarifying their complementary and distinct contributions. Our findings offer a novel networks-based perspective that harmonizes with (and compels an expansion of) the traditional taxonomy of curiosity.
{"title":"Curiosity as filling, compressing, and reconfiguring knowledge networks","authors":"Shubhankar P Patankar, Dale Zhou, Christopher W Lynn, Jason Z Kim, Mathieu Ouellet, Harang Ju, Perry Zurn, David M Lydon-Staley, Dani S Bassett","doi":"10.1177/26339137231207633","DOIUrl":"https://doi.org/10.1177/26339137231207633","url":null,"abstract":"Theoretical constructs, such as the information gap theory and compression progress theory, seek to explain how humans practice curiosity. According to the former, curiosity is the drive to acquire information missing from our understanding of the world. According to the latter, curiosity is the drive to construct parsimonious mental world models. To complement the densification and simplification processes inherent to these frameworks, we propose the conformational change theory, wherein we posit that curiosity builds mental models with marked conceptual flexibility. We formalize curiosity as a knowledge-network-building process to investigate each theoretical account for individuals and collectives. In knowledge networks, gaps can be identified as topological cavities, compression progress can be quantified using network compressibility, and flexibility can be measured as the number of conformational degrees of freedom. We find that curiosity fills gaps and constructs increasingly compressible and flexible knowledge networks. Across individuals and collectives, we determine the contexts in which each account is explanatory, clarifying their complementary and distinct contributions. Our findings offer a novel networks-based perspective that harmonizes with (and compels an expansion of) the traditional taxonomy of curiosity.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136160805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intelligence Everywhere is predicated on the seamless integration of INTERNET of Things (IoT) networks transporting a vast amount of data streams through many computing resources across an edge-to-cloud continuum, relying on the orchestration of distributed machine learning models. The result is an interconnected and collective intelligent ecosystem where devices, systems, services, and users work together to support IoT applications. This paper discusses the state-of-the-art research and the principles of the Intelligence Everywhere framework for enhancing IoT applications in vertical sectors such as Digital Health, Infrastructure, and Transportation/Mobility in the context of intelligent society (Society 5.0). It also introduces a novel perspective for the development of horizontal IoT applications, capable of running across various IoT networks while fostering collective intelligence across diverse sectors. Finally, this paper provides comprehensive insights into the challenges and opportunities for harnessing collective knowledge from real-time insights, leading to optimised processes and better overall collaboration across different IoT sectors.
{"title":"Fostering new vertical and horizontal IoT applications with intelligence everywhere","authors":"Hung Cao, Monica Wachowicz, Rene Richard, Ching-Hsien Hsu","doi":"10.1177/26339137231208966","DOIUrl":"https://doi.org/10.1177/26339137231208966","url":null,"abstract":"Intelligence Everywhere is predicated on the seamless integration of INTERNET of Things (IoT) networks transporting a vast amount of data streams through many computing resources across an edge-to-cloud continuum, relying on the orchestration of distributed machine learning models. The result is an interconnected and collective intelligent ecosystem where devices, systems, services, and users work together to support IoT applications. This paper discusses the state-of-the-art research and the principles of the Intelligence Everywhere framework for enhancing IoT applications in vertical sectors such as Digital Health, Infrastructure, and Transportation/Mobility in the context of intelligent society (Society 5.0). It also introduces a novel perspective for the development of horizontal IoT applications, capable of running across various IoT networks while fostering collective intelligence across diverse sectors. Finally, this paper provides comprehensive insights into the challenges and opportunities for harnessing collective knowledge from real-time insights, leading to optimised processes and better overall collaboration across different IoT sectors.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"179 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135849909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1177/26339137231203582
CB Knox, Steven Gray, Mahdi Zareei, Chelsea Wentworth, Payam Aminpour, Renee V Wallace, Jennifer Hodbod, Nathan Brugnone
Developing system understanding and testing interventions are critical steps to addressing wicked problems. Fuzzy cognitive mapping (FCM) can be a useful participatory modeling tool that enables aggregation of individual perspectives to build system models that represent groups’ collective intelligence (CI). However, current FCM aggregation methodologies for creating CI models have rarely been tested and compared. We conducted 51 FCM interviews with local experts in the Flint, MI food system to map their mental models about how different food system sectors influenced desirable outcomes. Using four differing aggregation techniques, based on experts’ identity diversity and cognitive diversity, we generated four CI models. The models were compared based on their similarity to real-world complex systems using performance metrics like network structure, micro-motifs, cognitive distance, and scenario outcomes. We found that using cognitive diversity to group individuals was better suited for modeling systems with diverse holders of knowledge.
{"title":"Modeling complex problems by harnessing the collective intelligence of local experts: New approaches in fuzzy cognitive mapping","authors":"CB Knox, Steven Gray, Mahdi Zareei, Chelsea Wentworth, Payam Aminpour, Renee V Wallace, Jennifer Hodbod, Nathan Brugnone","doi":"10.1177/26339137231203582","DOIUrl":"https://doi.org/10.1177/26339137231203582","url":null,"abstract":"Developing system understanding and testing interventions are critical steps to addressing wicked problems. Fuzzy cognitive mapping (FCM) can be a useful participatory modeling tool that enables aggregation of individual perspectives to build system models that represent groups’ collective intelligence (CI). However, current FCM aggregation methodologies for creating CI models have rarely been tested and compared. We conducted 51 FCM interviews with local experts in the Flint, MI food system to map their mental models about how different food system sectors influenced desirable outcomes. Using four differing aggregation techniques, based on experts’ identity diversity and cognitive diversity, we generated four CI models. The models were compared based on their similarity to real-world complex systems using performance metrics like network structure, micro-motifs, cognitive distance, and scenario outcomes. We found that using cognitive diversity to group individuals was better suited for modeling systems with diverse holders of knowledge.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135965099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01DOI: 10.1177/26339137231207634
Pierre Lévy
This paper presents IEML, Information Economy MetaLanguage, a constructed language with the same expressive power as a natural language and with computable semantics. Distinguished from pragmatic and referential semantics, linguistic semantics have not yet been completely formalized. Only its syntagmatic dimension has been mathematized in the form of regular languages. Its paradigmatic dimension remained to be formalized. In order to complete the mathematizing of language, including its paradigmatic dimension, I have coded linguistic semantics with IEML. This article introduces its 3000-word dictionary, its formal grammar, and its integrated tools for building semantic graphs. For the future, IEML could become a vector for a fluid calculation and communication of meaning—semantic interoperability—capable of de-compartmentalizing the digital memory, and of advancing the progress of collective intelligence, artificial intelligence, and digital humanities. I conclude by indicating some research directions.
{"title":"Semantic computing with IEML","authors":"Pierre Lévy","doi":"10.1177/26339137231207634","DOIUrl":"https://doi.org/10.1177/26339137231207634","url":null,"abstract":"This paper presents IEML, Information Economy MetaLanguage, a constructed language with the same expressive power as a natural language and with computable semantics. Distinguished from pragmatic and referential semantics, linguistic semantics have not yet been completely formalized. Only its syntagmatic dimension has been mathematized in the form of regular languages. Its paradigmatic dimension remained to be formalized. In order to complete the mathematizing of language, including its paradigmatic dimension, I have coded linguistic semantics with IEML. This article introduces its 3000-word dictionary, its formal grammar, and its integrated tools for building semantic graphs. For the future, IEML could become a vector for a fluid calculation and communication of meaning—semantic interoperability—capable of de-compartmentalizing the digital memory, and of advancing the progress of collective intelligence, artificial intelligence, and digital humanities. I conclude by indicating some research directions.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136168556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/26339137231199739
R. Reagans, Hagay C. Volvovsky, R. Burt
We reconcile two conflicting views of the network centralization effect on team performance. In one view, a centralized network is problematic because it limits knowledge transfer, making it harder for team members to discover productive combinations of their know-how and expertise. In the alternative view, the limits on knowledge transfer encourage search and experimentation, leading to the discovery of more valuable ideas. We maintain the two sides are not opposed but reflect two distinct ways centralization can affect a team’s shared problem-solving framework. The shared framework in our research is a shared language. We contend that team network centralization affects both how quickly a shared language emerges and the performance implications of the shared language that develops. We analyze the performance of 77 teams working to identify abstract symbols for 15 trials. Teams work under network conditions that vary with respect to centralization. Results indicate that centralized teams take longer to develop a shared language, but centralized teams also create a shared language that is more beneficial for performance. The findings also indicate that the highest performing teams are assigned to networks that combine elements of a centralized and a decentralized network.
{"title":"Shared language in the team network-performance association: Reconciling conflicting views of the network centralization effect on team performance","authors":"R. Reagans, Hagay C. Volvovsky, R. Burt","doi":"10.1177/26339137231199739","DOIUrl":"https://doi.org/10.1177/26339137231199739","url":null,"abstract":"We reconcile two conflicting views of the network centralization effect on team performance. In one view, a centralized network is problematic because it limits knowledge transfer, making it harder for team members to discover productive combinations of their know-how and expertise. In the alternative view, the limits on knowledge transfer encourage search and experimentation, leading to the discovery of more valuable ideas. We maintain the two sides are not opposed but reflect two distinct ways centralization can affect a team’s shared problem-solving framework. The shared framework in our research is a shared language. We contend that team network centralization affects both how quickly a shared language emerges and the performance implications of the shared language that develops. We analyze the performance of 77 teams working to identify abstract symbols for 15 trials. Teams work under network conditions that vary with respect to centralization. Results indicate that centralized teams take longer to develop a shared language, but centralized teams also create a shared language that is more beneficial for performance. The findings also indicate that the highest performing teams are assigned to networks that combine elements of a centralized and a decentralized network.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"119 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86830884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.1177/26339137231170022
D. Gordon, Daniel A. Levinthal
{"title":"On oracles and collective intelligence: An exchange of letters","authors":"D. Gordon, Daniel A. Levinthal","doi":"10.1177/26339137231170022","DOIUrl":"https://doi.org/10.1177/26339137231170022","url":null,"abstract":"","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84089079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.1177/26339137231176480
O. A. Acar
Science skepticism is widespread and on the rise. It is a strong threat to public well-being and global sustainability. In this paper, I argue that crowd science is a promising and underutilized tool to fight this threat. Drawing on recent behavioral research in marketing, I identify several positive psychological consequences of crowd science initiatives—both for the participants and observers of these initiatives—which could in turn promote stronger trust in science.
{"title":"Crowd science and science skepticism","authors":"O. A. Acar","doi":"10.1177/26339137231176480","DOIUrl":"https://doi.org/10.1177/26339137231176480","url":null,"abstract":"Science skepticism is widespread and on the rise. It is a strong threat to public well-being and global sustainability. In this paper, I argue that crowd science is a promising and underutilized tool to fight this threat. Drawing on recent behavioral research in marketing, I identify several positive psychological consequences of crowd science initiatives—both for the participants and observers of these initiatives—which could in turn promote stronger trust in science.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75128063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-01DOI: 10.1177/26339137231153078
J. See, Robert B. Rosenfeld, Sylvester Taylor, K. M. Wedic
An analogy is drawn between the study of human behavior and the study of plutonium to demonstrate that soft and hard sciences are more similar than different, making the distinction moot and unproductive. The studies of human behavior and plutonium follow a common scientific research cycle that aligns with Thomas Kuhn’s views of scientific change. This common research cycle provides evidence that the thought processes and methodologies required for success are congruent in the soft and hard sciences. The primary implication from this analogy is that scientists in all disciplines should eradicate the distinction between soft and hard sciences. Focusing on similarities rather than differences among researchers from different disciplines is necessary to enhance collective intelligence and the type of transdisciplinary collaboration required to tackle difficult sociotechnical problems. CCS Concepts: • Social and professional topics • User characteristics • Cultural characteristics.
{"title":"People are like plutonium","authors":"J. See, Robert B. Rosenfeld, Sylvester Taylor, K. M. Wedic","doi":"10.1177/26339137231153078","DOIUrl":"https://doi.org/10.1177/26339137231153078","url":null,"abstract":"An analogy is drawn between the study of human behavior and the study of plutonium to demonstrate that soft and hard sciences are more similar than different, making the distinction moot and unproductive. The studies of human behavior and plutonium follow a common scientific research cycle that aligns with Thomas Kuhn’s views of scientific change. This common research cycle provides evidence that the thought processes and methodologies required for success are congruent in the soft and hard sciences. The primary implication from this analogy is that scientists in all disciplines should eradicate the distinction between soft and hard sciences. Focusing on similarities rather than differences among researchers from different disciplines is necessary to enhance collective intelligence and the type of transdisciplinary collaboration required to tackle difficult sociotechnical problems. CCS Concepts: • Social and professional topics • User characteristics • Cultural characteristics.","PeriodicalId":93948,"journal":{"name":"Collective intelligence","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84222016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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