Artificial Intelligence and Law最新文献

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Toward representing interpretation in factor-based models of precedent 在基于因素的先例模型中体现解释力
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2024-01-12 DOI: 10.1007/s10506-023-09384-5
Adam Rigoni
{"title":"Toward representing interpretation in factor-based models of precedent","authors":"Adam Rigoni","doi":"10.1007/s10506-023-09384-5","DOIUrl":"10.1007/s10506-023-09384-5","url":null,"abstract":"<div><p>This article discusses the desirability and feasibility of modeling precedents with multiple interpretations within factor-based models of precedential constraint. The main idea is that allowing multiple reasonable interpretations of cases and modeling precedential constraint as a function of what all reasonable interpretations compel may be advantageous. The article explains the potential benefits of extending the models in this way with a focus on incorporating a theory of vertical precedent in U.S. federal appellate courts. It also considers the costs of extending the models in this way, such as the significant increase in the functional size of the case base and the need to provide some kind of ordering on interpretations to select a “best” interpretation. Finally, the article suggests partially incorporating multiple interpretations of dimensions as a realistic starting point for incorporating interpretations generally, and shows how doing so can help address difficulties with dimensions. The conclusion remarks on the use of interpretations to deal with inconsistent precedents.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 1","pages":"199 - 226"},"PeriodicalIF":3.1,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
DiscoLQA: zero-shot discourse-based legal question answering on European Legislation DiscoLQA:关于欧洲立法的基于零镜头话语的法律问题解答
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2024-01-10 DOI: 10.1007/s10506-023-09387-2
Francesco Sovrano, Monica Palmirani, Salvatore Sapienza, Vittoria Pistone
{"title":"DiscoLQA: zero-shot discourse-based legal question answering on European Legislation","authors":"Francesco Sovrano,&nbsp;Monica Palmirani,&nbsp;Salvatore Sapienza,&nbsp;Vittoria Pistone","doi":"10.1007/s10506-023-09387-2","DOIUrl":"10.1007/s10506-023-09387-2","url":null,"abstract":"<div><p>The structures of discourse used by legal and ordinary languages share differences that foster technical issues when applying or fine-tuning general-purpose language models for open-domain question answering on legal resources. For example, longer sentences may be preferred in European laws (i.e., Brussels I bis Regulation EU 1215/2012) to reduce potential ambiguities and improve comprehensibility, distracting a language model trained on ordinary English. In this article, we investigate some mechanisms to isolate and capture the discursive patterns of legalese in order to perform zero-shot question answering, i.e., without training on legal documents. Specifically, we use pre-trained open-domain answer retrieval systems and study what happens when changing the type of information to consider for retrieval. Indeed, by selecting only the important parts of discourse (e.g., elementary units of discourse, EDU for short, or abstract representations of meaning, AMR for short), we should be able to help the answer retriever identify the elements of interest. Hence, with this paper, we publish Q4EU, a new evaluation dataset that includes more than 70 questions and 200 answers on 6 different European norms, and study what happens to a baseline system when only EDUs or AMRs are used during information retrieval. Our results show that the versions using EDUs are overall the best, leading to state-of-the-art F1, precision, NDCG and MRR scores.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 2","pages":"323 - 359"},"PeriodicalIF":3.1,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09387-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139439836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A neural network to identify requests, decisions, and arguments in court rulings on custody 识别法院监护权裁决中的请求、决定和论据的神经网络
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2024-01-09 DOI: 10.1007/s10506-023-09380-9
José Félix Muñoz-Soro, Rafael del Hoyo Alonso, Rosa Montañes, Francisco Lacueva
{"title":"A neural network to identify requests, decisions, and arguments in court rulings on custody","authors":"José Félix Muñoz-Soro,&nbsp;Rafael del Hoyo Alonso,&nbsp;Rosa Montañes,&nbsp;Francisco Lacueva","doi":"10.1007/s10506-023-09380-9","DOIUrl":"10.1007/s10506-023-09380-9","url":null,"abstract":"<div><p>Court rulings are among the most important documents in all legal systems. This article describes a study in which natural language processing is used for the automatic characterization of Spanish judgments that deal with the physical custody (joint or individual) of minors. The model was trained to identify a set of elements: the type of custody requested by the plaintiff, the type of custody decided on by the court, and eight of the most commonly used arguments in this type of judgment. Two jurists independently annotated more than 3000 judgments, which were used to train a model based on transformers. The main difficulties encountered in this task were the complexity of the judicial language and the need to work with appellate court rulings that have a more complicated structure than decisions at first instance. For the complete court rulings, the F1 score of the inter-annotator agreement ranged from 0.60 to 0.86 and the Kappa index from 0.33 to 0.73. The F1 score of the agreement between the model and the annotators ranged from 0.66 to 0.93 and the Kappa index from 0.57 to 0.80. These results in which the model performance exceeds even the inter-annotator agreement show the high ability of transformers to identify abstract entities in legal texts.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 1","pages":"101 - 135"},"PeriodicalIF":3.1,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09380-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Cytomorphological traits of fine-needle aspirates of hyalinizing trabecular tumor of the thyroid gland: A brief report. 甲状腺透明小梁瘤细针穿刺细胞形态学特征:简要报告。
IF 1 2区 社会学
Artificial Intelligence and Law Pub Date : 2024-01-01 DOI: 10.4103/ijpm.ijpm_405_22
Fei Wang, Yufei Liu
{"title":"Cytomorphological traits of fine-needle aspirates of hyalinizing trabecular tumor of the thyroid gland: A brief report.","authors":"Fei Wang, Yufei Liu","doi":"10.4103/ijpm.ijpm_405_22","DOIUrl":"10.4103/ijpm.ijpm_405_22","url":null,"abstract":"<p><strong>Background: </strong>The incidence of thyroid tumor is increasing, and preoperative diagnosis of hyalinizing trabecular tumor (HTT) is difficult.</p><p><strong>Aim: </strong>To investigate the cytological features of HTT of the thyroid gland.</p><p><strong>Settings and design: </strong>A retrospective observational study.</p><p><strong>Materials and methods: </strong>Ultrasonography, preoperative needle aspiration cytology, postoperative histopathology, immunohistochemistry, and BRAF V600E gene test were performed in five patients with HTT to analyze the pathological characteristics of the patients and review the relevant literature.</p><p><strong>Results: </strong>Four female and one male patients with HTT were recruited. Fine-needle aspiration cytology (FNAC) showed bloodstained background tumor cells with multiple morphologies. The tumor cells exhibited ovoid nuclei, abundant cytoplasm, fine chromatin, nuclear crowding and overlapping, and small nucleoli. Focal nuclear pseudoinclusions and grooves were present. No papillary structures or psammoma bodies were observed. In all cases, tumor cells were radially distributed around the eosinophilic extracellular matrix. In 40% (2 in 5) of cases, trabecular patterns of elongated tumor cells were present, with their nuclei staggered along the longitudinal axis of tumor cells in the trabeculae. FNAC suggested two cases of HTT and three cases of papillary thyroid cancer. Post-operational biopsy indicated they were HTT cases.</p><p><strong>Conclusion: </strong>HTT is a rare thyroid tumor with non-specific clinical manifestations. It can be misinterpreted as papillary thyroid carcinoma by FNAC. However, its cytomorphological traits are helpful in the diagnosis. In combination with FNAC, immunohistochemistry, and molecular testing, HTT can be accurately diagnosed.</p>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"27 1","pages":"128-132"},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70762852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Automating petition classification in Brazil’s legal system: a two-step deep learning approach 巴西法律系统中的请愿分类自动化:两步式深度学习方法
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2023-12-15 DOI: 10.1007/s10506-023-09385-4
Yuri D. R. Costa, Hugo Oliveira, Valério Nogueira Jr., Lucas Massa, Xu Yang, Adriano Barbosa, Krerley Oliveira, Thales Vieira
{"title":"Automating petition classification in Brazil’s legal system: a two-step deep learning approach","authors":"Yuri D. R. Costa,&nbsp;Hugo Oliveira,&nbsp;Valério Nogueira Jr.,&nbsp;Lucas Massa,&nbsp;Xu Yang,&nbsp;Adriano Barbosa,&nbsp;Krerley Oliveira,&nbsp;Thales Vieira","doi":"10.1007/s10506-023-09385-4","DOIUrl":"10.1007/s10506-023-09385-4","url":null,"abstract":"<div><p>Automated classification of legal documents has been the subject of extensive research in recent years. However, this is still a challenging task for long documents, since it is difficult for a model to identify the most relevant information for classification. In this paper, we propose a two-stage supervised learning approach for the classification of petitions, a type of legal document that requests a court order. The proposed approach is based on a word-level encoder–decoder Seq2Seq deep neural network, such as a Bidirectional Long Short-Term Memory (BiLSTM) or a Bidirectional Encoder Representations from Transformers (BERT) model, and a document-level Support Vector Machine classifier. To address the challenges posed by the lengthy legal documents, the approach introduces a human-in-the-loop approach, whose task is to localize and tag relevant segments of text in the word-level training part, which dramatically reduces the dimension of the document classifier input vector. We performed experiments to validate our approach using a real-world dataset comprised of 270 intermediate petitions, which were carefully annotated by specialists from the 15th civil unit of the State of Alagoas, Brazil. Our results revealed that both BiLSTM and BERT-Convolutional Neural Networks variants achieved an accuracy of up to 95.49%, and also outperformed baseline classifiers based on the Term Frequency–Inverse Document Frequency test vectorizer. The proposed approach is currently being utilized to automate the aforementioned justice unit, thereby increasing its efficiency in handling repetitive tasks.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 1","pages":"227 - 251"},"PeriodicalIF":3.1,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138999292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reasoning with inconsistent precedents 根据不一致的先例进行推理
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2023-12-14 DOI: 10.1007/s10506-023-09382-7
Ilaria Canavotto
{"title":"Reasoning with inconsistent precedents","authors":"Ilaria Canavotto","doi":"10.1007/s10506-023-09382-7","DOIUrl":"10.1007/s10506-023-09382-7","url":null,"abstract":"<div><p>Computational models of legal precedent-based reasoning developed in AI and Law are typically based on the simplifying assumption that the background set of precedent cases is consistent. Besides being unrealistic in the legal domain, this assumption is problematic for recent promising applications of these models to the development of explainable AI methods. In this paper I explore a model of legal precedent-based reasoning that, unlike existing models, does not rely on the assumption that the background set of precedent cases is consistent. The model is a generalization of the reason model of precedential constraint. I first show that the model supports an interesting deontic logic, where consistent obligations can be derived from inconsistent case bases. I then provide an explanation of this surprising result by proposing a reformulation of the model in terms of cases that support a new potential decision and cases that conflict with it. Finally, I show that the reformulation of the model allows us to verify that inconsistent case bases do not make verification that a decision is permissible substantially more complex than consistent case bases and to introduce intuitive criteria to compare different permissible decisions.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 1","pages":"137 - 166"},"PeriodicalIF":3.1,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139002537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Decision support for detecting sensitive text in government records 为检测政府档案中的敏感文本提供决策支持
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2023-12-10 DOI: 10.1007/s10506-023-09383-6
Karl Branting, Bradford Brown, Chris Giannella, James Van Guilder, Jeff Harrold, Sarah Howell, Jason R. Baron
{"title":"Decision support for detecting sensitive text in government records","authors":"Karl Branting,&nbsp;Bradford Brown,&nbsp;Chris Giannella,&nbsp;James Van Guilder,&nbsp;Jeff Harrold,&nbsp;Sarah Howell,&nbsp;Jason R. Baron","doi":"10.1007/s10506-023-09383-6","DOIUrl":"10.1007/s10506-023-09383-6","url":null,"abstract":"<div><p>Freedom of information laws promote transparency by permitting individuals and organizations to obtain government documents. However, exemptions from disclosure are necessary to protect privacy and to permit government officials to deliberate freely. Deliberative language is often the most challenging and burdensome exemption to detect, leading to high processing costs and delays in responding to open-records requests. This paper describes a novel deliberative-language detection model trained on a new annotated training set. The deliberative-language detection model is a component of a decision-support system for open-records requests under the US Freedom of Information Act, the <i>FOIA Assistant</i>, that ingests documents responsive to an open-records requests, suggests passages likely to be subject to deliberative language, privacy, or other exemptions, and assists analysts in rapidly redacting suggested passages. The tool’s interface is based on extensive human-factors and usability studies with analysts and is currently in operational testing by multiple US federal agencies.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 1","pages":"171 - 197"},"PeriodicalIF":3.1,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09383-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138982323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhancing legal judgment summarization with integrated semantic and structural information 利用综合语义和结构信息加强法律判决摘要分析
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2023-11-26 DOI: 10.1007/s10506-023-09381-8
Jingpei Dan, Weixuan Hu, Yuming Wang
{"title":"Enhancing legal judgment summarization with integrated semantic and structural information","authors":"Jingpei Dan,&nbsp;Weixuan Hu,&nbsp;Yuming Wang","doi":"10.1007/s10506-023-09381-8","DOIUrl":"10.1007/s10506-023-09381-8","url":null,"abstract":"<div><p>Legal Judgment Summarization (LJS) can highly summarize legal judgment documents, improving judicial work efficiency in case retrieval and other occasions. Legal judgment documents are usually lengthy; however, most existing LJS methods are directly based on general text summarization models, which cannot handle long texts effectively. Additionally, due to the complex structural characteristics of legal judgment documents, some information may be lost by applying only one single kind of summarization model. To address these issues, we propose an integrated summarization method which leverages both semantic and structural information to improve the quality of LJS. Specifically, legal judgment documents are firstly segmented into three relatively short parts according to their specific structure. We propose an extractive summarization model named BSLT and an abstractive summarization model named LPGN by adopting Lawformer as the encoder. Lawformer is a new pre-trained language model for long legal documents, which specializes in capturing long-distance dependency and modeling legal semantic features. Then, we adopt different models to summarize the corresponding part regarding its structural characteristics. Finally, the obtained summaries are integrated to generate a high-quality summary involving semantic and structural information. We conduct comparative experiments to evaluate the performance of our model. The results show that our model outperforms the baseline model LEAD-3 by 14.78% on the mean ROUGE score, which demonstrates our method is effective in LJS and is prospected to be applied to assist other tasks in legal artificial intelligence.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 2","pages":"271 - 292"},"PeriodicalIF":3.1,"publicationDate":"2023-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139235415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Automated legal reasoning with discretion to act using s(LAW) 使用 s(LAW)进行自动法律推理并酌情采取行动
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2023-11-20 DOI: 10.1007/s10506-023-09376-5
Joaquín Arias, Mar Moreno-Rebato, Jose A. Rodriguez-García, Sascha Ossowski
{"title":"Automated legal reasoning with discretion to act using s(LAW)","authors":"Joaquín Arias,&nbsp;Mar Moreno-Rebato,&nbsp;Jose A. Rodriguez-García,&nbsp;Sascha Ossowski","doi":"10.1007/s10506-023-09376-5","DOIUrl":"10.1007/s10506-023-09376-5","url":null,"abstract":"<div><p>Automated legal reasoning and its application in smart contracts and automated decisions are increasingly attracting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to <i>justify</i> in human-understandable terms the advice given. Logic Programming, specially Answer Set Programming, has a rich semantics and has been used to very concisely express complex knowledge. However, modelling <i>discretionality to act</i> and other vague concepts such as <i>ambiguity</i> cannot be expressed in top-down execution models based on Prolog, and in bottom-up execution models based on ASP the justifications are incomplete and/or not scalable. We propose to use s(CASP), a top-down execution model for predicate ASP, to model vague concepts following a set of patterns. We have implemented a framework, called s(LAW), to model, reason, and justify the applicable legislation and validate it by translating (and benchmarking) a representative use case, the criteria for the admission of students in the “Comunidad de Madrid”.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 4","pages":"1141 - 1164"},"PeriodicalIF":3.1,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139255770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Bringing order into the realm of Transformer-based language models for artificial intelligence and law 将秩序带入基于 Transformer 的人工智能和法律语言模型领域
IF 3.1 2区 社会学
Artificial Intelligence and Law Pub Date : 2023-11-20 DOI: 10.1007/s10506-023-09374-7
Candida M. Greco, Andrea Tagarelli
{"title":"Bringing order into the realm of Transformer-based language models for artificial intelligence and law","authors":"Candida M. Greco,&nbsp;Andrea Tagarelli","doi":"10.1007/s10506-023-09374-7","DOIUrl":"10.1007/s10506-023-09374-7","url":null,"abstract":"<div><p>Transformer-based language models (TLMs) have widely been recognized to be a cutting-edge technology for the successful development of deep-learning-based solutions to problems and applications that require natural language processing and understanding. Like for other textual domains, TLMs have indeed pushed the state-of-the-art of AI approaches for many tasks of interest in the legal domain. Despite the first Transformer model being proposed about six years ago, there has been a rapid progress of this technology at an unprecedented rate, whereby BERT and related models represent a major reference, also in the legal domain. This article provides the first systematic overview of TLM-based methods for AI-driven problems and tasks in the legal sphere. A major goal is to highlight research advances in this field so as to understand, on the one hand, how the Transformers have contributed to the success of AI in supporting legal processes, and on the other hand, what are the current limitations and opportunities for further research development.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 4","pages":"863 - 1010"},"PeriodicalIF":3.1,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09374-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142519046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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