Damaged goods: riskiness and LGBT young peoples’ interactions with police

For some time now, research has suggested lesbian, gay, bisexual and transgender (LGBT) young people are ‘at-risk’ of victimisation and legally ‘risky’. Relatively few studies have examined how ‘risk factor’ research influences the everyday lives of LGBT young people. This paper reports how the experiences of police by 35 LGBT young people in Brisbane, Queensland reflected discourses about LGBT riskiness and how danger informed their interactions with police in public spaces. The participants specifically note how looking at-risk or looking risky affected their experiences of policing. The paper will conclude with recommendations for improved future policing practice.

Catch me if you can : the effective service of bankruptcy documents in a changing world

This article discusses some recent judicial decisions to assist legal practitioners to overcome some of the problems encountered when serving Bankruptcy Notices and Creditor’s Petitions. Some of the issues covered in the discussion are: What the valid last-known address of the debtor can be, whether a Bankruptcy Notice can be validly served by email on a debtor who is located outside Australia, whether service of a Bankruptcy Notice is valid when the debtor is outside Australia when service on the debtor occurs in Australia, whether the creditor’s failure to obtain leave for service of a Bankruptcy Notice can be excused, what can be done regarding personal service of a Creditor’s Petition when a debtor is outside Australia and whether the Court can set aside a sequestration order. The article goes on to place the issues in the context of broader bankruptcy policies noting that effective service of bankruptcy documents is challenging in a world where mobility of debtors is global and new modes of communication ever changing.

Multi-version documents : A digitisation solution for textual cultural artefacts

Textual cultural heritage artefacts present two serious problems for the encoder: how to record different or revised versions of the same work, and how to encode conflicting perspectives of the text using markup. Both are forms of textual variation, and can be accurately recorded using a multi-version document, based on a minimally redundant directed graph that cleanly separates variation from content.

Perspectives on the recruitment and retention of culturally and linguistically diverse police

Purpose: The aims of this paper are: to investigate the perceptions held by police (insiders) and community member (outsiders) of the recruitment and retention of culturally and linguistically diverse employees of Victoria Police; and, to develop a model that can assist in future recruitment and retention policy development.---------- Design/methodology/approach: Structured focus group interviews were conducted based on an instrument deduced from existing literature. Police and community members were interviewed separate cohorts. The discussions were thematically coded to themes and sub-themes.---------- Findings: Specific differences were identified in perceptions of the importance of recruiting culturally and linguistically diverse groups, barriers to recruitment, recruitment methods, and retention methods.---------- Research limitations/implications: Based on these perceptions, a propose a model addresses the importance of cultural diversity in policing and barriers to recruitment and retention of culturally and linguistically diverse employees. Further research is necessary to assess the broader applicability of this model.---------- Practical implications: The proposed model is may be used as the basis for future recruitment and retention activities, and human resource management policy development.---------- Originality/value: This is the first study in the Australian context of recruitment and retention of culturally and linguistically diverse police that addresses both community and police perspectives. Aligning the demographic profile of the police service with that of the community is beneficial to effective policing.

XML documents clustering using tensor space model - A preliminary study

A hierarchical structure is used to represent the content of the semi-structured documents such as XML and XHTML. The traditional Vector Space Model (VSM) is not sufficient to represent both the structure and the content of such web documents. Hence in this paper, we introduce a novel method of representing the XML documents in Tensor Space Model (TSM) and then utilize it for clustering. Empirical analysis shows that the proposed method is scalable for a real-life dataset as well as the factorized matrices produced from the proposed method helps to improve the quality of clusters due to the enriched document representation with both the structure and the content information.

Queer cops in Queensland : exploring LGBT narrative histories in the Queensland Police Service

This chapter reports on a narrative project recording the experiences of LGBT former and current police officers in the Queensland Police Service (QPS), Australia. It begins by examining the historical and research contexts of LGBT police officers, followed by a discussion of the methodology employed for the project. The chapter then examines and analyzes key themes emerging from the data about coming out, macho police culture, and the double life syndrome often experienced by LGBT police officers. Finally, it suggests that further research might uncover a more widespread application of these findings.

Overview of the INEX 2010 XML mining track : clustering and classification of XML documents

The XML Document Mining track was launched for exploring two main ideas: (1) identifying key problems and new challenges of the emerging field of mining semi-structured documents, and (2) studying and assessing the potential of Machine Learning (ML) techniques for dealing with generic ML tasks in the structured domain, i.e., classification and clustering of semi-structured documents. This track has run for six editions during INEX 2005, 2006, 2007, 2008, 2009 and 2010. The first five editions have been summarized in previous editions and we focus here on the 2010 edition. INEX 2010 included two tasks in the XML Mining track: (1) unsupervised clustering task and (2) semi-supervised classification task where documents are organized in a graph. The clustering task requires the participants to group the documents into clusters without any knowledge of category labels using an unsupervised learning algorithm. On the other hand, the classification task requires the participants to label the documents in the dataset into known categories using a supervised learning algorithm and a training set. This report gives the details of clustering and classification tasks.

Corrupt police networks : uncovering hidden relationship patterns, functions and roles

This article applies social network analysis techniques to a case study of police corruption in order to produce findings which will assist in corruption prevention and investigation. Police corruption is commonly studied but rarely are sophisticated tools of analyse engaged to add rigour to the field of study. This article analyses the ‘First Joke’ a systemic and long lasting corruption network in the Queensland Police Force, a state police agency in Australia. It uses the data obtained from a commission of inquiry which exposed the network and develops hypotheses as to the nature of the networks structure based on existing literature into dark networks and criminal networks. These hypotheses are tested by entering the data into UCINET and analysing the outcomes through social network analysis measures of average path distance, centrality and density. The conclusions reached show that the network has characteristics not predicted by the literature.

XML documents clustering using Tensor Space Model

The traditional Vector Space Model (VSM) is not able to represent both the structure and the content of XML documents. This paper introduces a novel method of representing XML documents in a Tensor Space Model (TSM) and then utilizing it for clustering. Empirical analysis shows that the proposed method is scalable for large-sized datasets; as well, the factorized matrices produced from the proposed method help to improve the quality of clusters through the enriched document representation of both structure and content information.

‘It’s not like we’re going to jump them’: how transgressing heteronormativity shapes police interactions with LGBT young people

This paper explores how visibly transgressing heteronormativity shapes police interactions with LGBT young people. While research evidences how sexually and gender diverse bodies can be abused in schools, policing is overlooked. Interviews with 35 LGBT young people demonstrate how bodies transgressing heteronormativity (that is, non-heteronormative bodies) mediate their policing experiences in Queensland, Australia. Drawing on Foucault, Butler, and others, the paper suggests police interactions and use of discretion with LGBT young people was informed by non-heteronormative bodies discursively performing queerness in ways read by police. The paper concludes noting tensions produced for youthful LGBT bodies in public spaces.

Selected new training documents to update user profile

Relevance Feedback (RF) has been proven very effective for improving retrieval accuracy. Adaptive information filtering (AIF) technology has benefited from the improvements achieved in all the tasks involved over the last decades. A difficult problem in AIF has been how to update the system with new feedback efficiently and effectively. In current feedback methods, the updating processes focus on updating system parameters. In this paper, we developed a new approach, the Adaptive Relevance Features Discovery (ARFD). It automatically updates the system's knowledge based on a sliding window over positive and negative feedback to solve a nonmonotonic problem efficiently. Some of the new training documents will be selected using the knowledge that the system currently obtained. Then, specific features will be extracted from selected training documents. Different methods have been used to merge and revise the weights of features in a vector space. The new model is designed for Relevance Features Discovery (RFD), a pattern mining based approach, which uses negative relevance feedback to improve the quality of extracted features from positive feedback. Learning algorithms are also proposed to implement this approach on Reuters Corpus Volume 1 and TREC topics. Experiments show that the proposed approach can work efficiently and achieves the encouragement performance.