Threat Assessment in Community Colleges

Threat assessment and management in higher education is still in the early stages of development. Little is noted in the research literature about the practices of threat assessment teams in this environment, particularly in community colleges. To fill this knowledge gap, a random national sample of 15% (n = 148) of public community colleges were surveyed as to: (a) threat assessment practices, (b) continuing education needs, and (c) training delivery preferences. Lead threat assessment practitioners were surveyed from those institutions. A total of 113 participants returned a completed survey. This number represented a return rate of 76%. A post hoc power analysis reported an actual power (i.e., 1-β error probability) of 0.84. The professional breakdown of respondents was law enforcement/security (n = 52), college administration (n = 55) and other (n = 6). The vast majority (73%) of the community colleges operated with a formalized threat assessment team, yet 67% of respondents reported fewer than 40 hours of threat assessment training. The leading types of team composition were: (a) employees only (57%), and (b) mix of employees and outside personnel (32%). Most college threat assessment teams addressed more than just students as threat sources (69%). The top continuing education needs reported ranged from legal implications to advanced training of threat assessment and management. Inferential statistical analyses revealed that, in reference to their professional background, threat assessment practitioners similarly rank their: (a) continuing education needs, and (b) training delivery preferences (i.e., in person vs. online). Implications for both research and practice were discussed.

An Empirical Comparison of Portuguese and Multilingual BERT Models for Auto-Classification of NCM Codes in International Trade

Classification problems are common activities in many different domains and supervised learning algorithms have shown great promise in these areas. The classification of goods in international trade in Brazil represents a real challenge due to the complexity involved in assigning the correct category codes to a good, especially considering the tax penalties and legal implications of a misclassification. This work focuses on the training process of a classifier based on bidirectional encoder representations from transformers (BERT) for tax classification of goods with MCN codes which are the official classification system for import and export products in Brazil. In particular, this article presents results from using a specific Portuguese-language-pretrained BERT model, as well as results from using a multilingual-pretrained BERT model. Experimental results show that Portuguese model had a slightly better performance than the multilingual model, achieving an MCC 0.8491, and confirms that the classifiers could be used to improve specialists’ performance in the classification of goods.

AL-HUQUL AD-DALALIYAH WA MUKAWWINAH AL-MA’NA LI FI’LI “THAHHARA” FI AL-QUR’AN

The field of meaning and components is one of the bases for understanding the meaning of a language. And this theory helps researchers uncover the meaning of the verb 'to purify' in Arabic. The verb 'to purify' was chosen because it has an essential meaning for Muslims. Even the Qur'an uses various lexemes for this verb by looking at its context, subject, and function because different lexemes carry different meanings and legal implications. Therefore, this study aims to describe the meaning field, analyze the meaning component, and explain the verbum component of the verb 'to purify' in Arabic. This research is descriptive qualitative research. The data were collected from the Qur'an, with selective reading and note-taking techniques. The data analysis technique uses an interactive model by Miles and Huberman, namely by collecting data from sources and sorting it then selecting it to produce findings of four purifying verbs, namely طَهَّرَ، قَدَّسَ، زَكَّى،, and سَبَّحَ. The following components of meaning are determined through the type of subject, object, characteristics of the object, and how it works and concluded that the verb 'to purify' is included in the category of state verb and process verb. The field of meaning and components is one of the bases for understanding the meaning of a language. And this theory helps researchers uncover the meaning of the verb 'to purify' in Arabic. The verb 'to purify' was chosen because it has an essential meaning for Muslims. Even the Qur'an uses various lexemes for this verb by looking at its context, subject, and function because different lexemes carry different meanings and legal implications. Therefore, this study aims to describe the meaning field, analyze the meaning component, and explain the verbum component of the verb 'to purify' in Arabic. This research is descriptive qualitative research. The data were collected from the Qur'an, with selective reading and note-taking techniques. The data analysis technique uses an interactive model by Miles and Huberman, namely by collecting data from sources and sorting it then selecting it to produce findings of four purifying verbs, namely طَهَّرَ، قَدَّسَ، زَكَّى،, and سَبَّحَ. The following components of meaning are determined through the type of subject, object, characteristics of the object, and how it works and concluded that the verb 'to purify' is included in the category of state verb and process verb.

Moral Obligation to Worship God Alone

This article examines how, in his al-Tafsīr al-kabīr, Fakhr al-Dīn al-Rāzī (d. 606/1210) addresses the problem of the obligation to thank the benefactor (wujūb shukr al-munʿim) within the context of the Quranic command to worship God alone. The obligation to thank one’s benefactor was a contentious problem among classical Islamic thinkers before Rāzī, and it was frequently discussed in fiqh and kalām works in the context of the ontology and epistemology of moral values and legal norms. Rāzī’s analysis in the Tafsīr, however, sheds light on another way in which the “thanking one’s benefactor”-problem was of relevance for classical Islamic thinkers: it is used to frame the rationale for monotheism in terms of the gratitude God deserves for being humans’ provider. This aspect of the “thanking one’s benefactor”-problem has not been highlighted in the secondary literature. This article discusses how Rāzī’s analysis of God’s sole deservedness of worship has theological, legal, and ethical/moral implications. The theological implications are found in the questions it raises about the notorious problem of causality. The legal implications become apparent in Rāzī’s interest in the ratio legis of the Quranic command and in establishing that the obligation arises with God’s sovereign decree. The ethical or moral implications, finally, are seen in his concern with how humans come to know of the goodness of monotheism and the repugnancy of polytheism. The article contextualises Rāzī’s position in the Tafsīr against the background of the fiqh and kalām debates about the “thanking one’s benefactor”-problem.

Hate Speech Detection Using Text Mining and Machine Learning

Automatic hate speech detection on social media is becoming an outstanding concern in modern countries. Indeed, hate speech towards people brings about violent acts and social chaos, hence law prohibits it, and it engenders moral and legal implications. It is crucial that we can precisely categorize the hate speech, and not a hate speech automatically, while this allows us to identify easily real people who represent a threat for our society, and who wrongly regard as hateful speakers. In this paper, we applied a complete text mining process and Naïve Bayes machine learning classification algorithm to two different data sets (tweets_Num1 and tweets_Num2) taken from Twitter, to better classify tweets. The results obtained demonstrate that our model performed well regarding different metrics based on the confusion matrix including the accuracy metric, which achieved 87. 23% on the first dataset, and 93. 06% on the second.

Legal implications of the criminal policy of returning state financial losses by corporations in corruption criminal acts to restore state financial losses

The criminal policy of returning state financial losses to corporations as perpetrators of corruption in state financial losses is regulated as additional criminal sanctions in the form of confiscation of goods and payment of replacement money in Article 18 paragraph (1) letter a and letter b of Law Number 31 of 1999 as amended by Law Number 20 of 2001 concerning the Eradication of Corruption Crimes. The purpose of this study is to find out how the legal implications of the criminal policy of returning state financial losses by corporations as perpetrators of criminal acts of corruption are. This research includes normative legal research with several approaches, namely; Historical approach, statutory approach, case approach, and conceptual approach. The results of this study show that the existing criminal policy for recovering state financial losses still has various legal implications which result in non-optimal efforts to recover state financial losses due to corruption in state financial losses committed by corporations.