An evaluation of Home Office extended interviews for police personnel

In this thesis the validity of an Assessment Centre (called 'Extended Interview') operated on behalf of the British police is investigated. This Assessment Centre (AC) is used to select from amongst internal candidates (serving policemen and policewomen) and external candidates (graduates) for places on an accelerated promotion scheme. The literature is reviewed with respect to history, content, structure, reliability, validity, efficiency and usefulness of ACs, and to contextual issues surrounding AC use. The history of, background to and content of police Extended Interviews (Els) is described, and research issues are identified. Internal validation involved regression of overall EI grades on measures from component tests, exercises, interviews and peer nominations. Four samples numbering 126, 73, 86 and 109 were used in this part of the research. External validation involved regression of three types of criteria - training grades, rank attained, and supervisory ratings - on all EI measures. Follow-up periods for job criteria ranged from 7 to 19 years. Three samples, numbering 223, 157 and 86, were used in this part of the research. In subsidiary investigations, supervisory ratings were factor analysed and criteria intercorrelated. For two of the samples involved in the external validition, clinical/judgemental prediction was compared with mechanical (unit-weighted composite) prediction. Main conclusions are that: (1) EI selection decisions were valid, but only for a job performance criterion; relatively low validity overall was interpreted principally in terms of the questionable job relatedness of the EI procedure; (2) Els as a whole had more validity than was reflected in final EI decisions; (3) assessors' use of information was not optimum, tending to over-emphasize subjectively derived information particularly from interviews; and (4) mechanical prediction was superior to clinical/judgemental prediction for five major criteria.

Guilty as charged? The effect of interpreting on interviews with suspects

The ways in which an interpreter affects the processes and, possibly, the outcomes of legal proceedings has formed the focus of much recent research, most of it centred upon courtroom discourse. However comparatively little research has been carried out into the effect of interpreting on the interview with a suspect, despite its 'upstream' position in the legal process and vital importance as evidence. As a speech event in the judicial system, the interview differs radically from that which takes place 'downstream', that is, in court. The interview with suspect represents an entirely different construct, in which a range of registers is apparent, and participants use distinctive means to achieve their institutional goals. When a transcript of an interpreter-mediated interview is read out in court, it is assumed that this is a representation of an event, which is essentially identical to a monolingual interview. This thesis challenges that assumption. Using conservation analytic techniques, it examines data from a corpus of monolingual and interpreter-mediated, taped interviews with suspects, in order to identify potentially significant interactional differences and describe ways in which the interpreter affects the processes and may affect the outcomes of the interview. It is argued that although individually, the interactional differences may appear slight, their cumulative effect is significant, particularly since the primary participants in the event are unaware of the full force of the interpreting effect. Finally, the thesis suggests that the insights provided by linguistic analysis of the interpreting on interviews may provide the basis for training, both for interpreters themselves, and for officers in techniques for interpreter-mediated interviews.

Police interviews with women reporting rape: a critical discourse analysis

This study investigates the discursive patterns of interactions between police interviewers and women reporting rape in significant witness interviews. Data in the form of video recorded interviews were obtained from a UK police force for the purposes of this study. The data are analysed using a multi-method approach, incorporating tools from micro-sociology, Conversation Analysis and Discursive Psychology, to reveal patterns of interactional control, negotiation, and interpretation. The study adopts a critical approach, which is to say that as well as describing discursive patterns, it explains them in light of the discourse processes involved in the production and consumption of police interview talk, and comments on the relationship between these discourse processes and the social context in which they occur. A central focus of the study is how interviewers draw on particular interactional resources to shape interviewees? accounts in particular ways, and this is discussed in relation to the institutional role of the significant witness interview. The discussion is also extended to the ways in which mainstream rape ideology is both reflected in, and maintained by, the discursive choices of participants. The findings of this study indicate that there are a number of issues to be addressed in terms of the training currently offered to officers at Level 2 of the Professionalising Investigation Programme (PIP) (NPIA, 2009) who intend to conduct significant witness interviews. Furthermore, a need is identified to bring the linguistic and discursive processes of negotiation and transformation identified by the study to the attention of the justice system as a whole. This is a particularly pressing need in light of judicial reluctance to replace written witness statements, the current ���end product? of significant witness interviews, with the video recorded interview in place of direct examination in cases of rape.

Weakly supervised joint sentiment-topic detection from text

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework called joint sentiment-topic (JST) model based on latent Dirichlet allocation (LDA), which detects sentiment and topic simultaneously from text. A reparameterized version of the JST model called Reverse-JST, obtained by reversing the sequence of sentiment and topic generation in the modeling process, is also studied. Although JST is equivalent to Reverse-JST without a hierarchical prior, extensive experiments show that when sentiment priors are added, JST performs consistently better than Reverse-JST. Besides, unlike supervised approaches to sentiment classification which often fail to produce satisfactory performance when shifting to other domains, the weakly supervised nature of JST makes it highly portable to other domains. This is verified by the experimental results on data sets from five different domains where the JST model even outperforms existing semi-supervised approaches in some of the data sets despite using no labeled documents. Moreover, the topics and topic sentiment detected by JST are indeed coherent and informative. We hypothesize that the JST model can readily meet the demand of large-scale sentiment analysis from the web in an open-ended fashion.

Feature LDA:a supervised topic model for automatic detection of web API documentations from the web

Web APIs have gained increasing popularity in recent Web service technology development owing to its simplicity of technology stack and the proliferation of mashups. However, efficiently discovering Web APIs and the relevant documentations on the Web is still a challenging task even with the best resources available on the Web. In this paper we cast the problem of detecting the Web API documentations as a text classification problem of classifying a given Web page as Web API associated or not. We propose a supervised generative topic model called feature latent Dirichlet allocation (feaLDA) which offers a generic probabilistic framework for automatic detection of Web APIs. feaLDA not only captures the correspondence between data and the associated class labels, but also provides a mechanism for incorporating side information such as labelled features automatically learned from data that can effectively help improving classification performance. Extensive experiments on our Web APIs documentation dataset shows that the feaLDA model outperforms three strong supervised baselines including naive Bayes, support vector machines, and the maximum entropy model, by over 3% in classification accuracy. In addition, feaLDA also gives superior performance when compared against other existing supervised topic models.

Topic continuity in informal conversations between native and non-native speakers of English

Topic management by non-native speakers (NNSs) during informal conversations has received comparatively little attention from researchers, and receives surprisingly little attention in second language learning and teaching. This article reports on one of the topic management strategies employed by international students during informal, social interactions with native-speaker peers, exploring the process of maintaining topic continuity following temporary suspensions of topics. The concept of side sequences is employed to illustrate the nature of different types of topic suspension, as well as the process of jointly negotiating a return to the topic. Extracts from the conversations show that such sequences were not exclusively occasioned by language difficulties, and that the non-native speaker participants were able to effect successful returns to the main topic of the conversations.

Joint sentiment/topic model for sentiment analysis

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet Allocation (LDA), called joint sentiment/topic model (JST), which detects sentiment and topic simultaneously from text. Unlike other machine learning approaches to sentiment classification which often require labeled corpora for classifier training, the proposed JST model is fully unsupervised. The model has been evaluated on the movie review dataset to classify the review sentiment polarity and minimum prior information have also been explored to further improve the sentiment classification accuracy. Preliminary experiments have shown promising results achieved by JST.

The question of question types in police interviews:a review of the literature from a psychological and linguistic perspective

The past two decades has seen a plethora of papers and academic research conducted on investigative interviews with victims, witnesses and suspected offenders, with a particular focus on questioning techniques and typologies. However, despite this research, there still remain significant discrepancies amongst academic researchers and practitioners over how best to describe types of questions. This article considers the available literature relating to interviews with children and adults from both a psychological and linguistic perspective. In particular, we examine how different types of questions are described, and explore the discrepancies between competing definitions. �� 2010, equinox publishing.

Information and behavioural instruction along the health-care pathway:the perspective of people undergoing hernia repair surgery and the role of formal and informal information sources

Provision of information and behavioural instruction has been demonstrated to improve recovery after surgery. However, patients draw on a range of information sources and it is important to establish which sources patients use and how this influences perceptions and behaviour as they progress along the surgical pathway. In this qualitative, exploratory and longitudinal study, the use of information and instruction were explored from the perspective of people undergoing inguinal hernia repair surgery. Seven participants undergoing inguinal hernia repair surgery were interviewed using semi-structured interviews 2 weeks before surgery and 2 weeks and 4 months post-surgery. Nineteen interviews were conducted in total. Topic guides included sources of knowledge, reasons for help-seeking and opting for surgery and factors influencing return to activity. Data were analysed thematically according to Interpretative Phenomenological Analysis. Participants sought information from a range of sources, focusing on informal information sources before surgery and using information and instruction from health-care professionals post-surgery. This information influenced behaviours including deciding to undergo surgery, use of pain medication and returning to usual activity. Anxiety and help-seeking resulted when unexpected post-surgical events occurred such as extensive bruising. Findings were consistent with psychological and sociological theories. Overall, participants were positive about the information and instruction they received but expressed a desire for more timely information on post-operative adverse events.

The social and entrepreneurial orientation of small businesses:mutually exclusive or reinforcing? (INTERACTIVE PAPER)

Principal Topic - As argued by Acs and Phillips (2002) it is not only ���the creation of wealth (entrepreneurship)��� but also ���the reconstitution of wealth (philanthropy)���, which has been essential for the inherent dynamism of the market economy (Ibid., p.201). However, we understand little about the entrepreneurship ��� philanthropy link in institutional contexts that differ from that of leading developed market economies. Accordingly our research agenda is to investigate the entrepreneurship-philanthropy nexus in a very different context of Lithuania, a country which shed a command economy system twenty years ago. In particular, we are interested to see if the cluster of attitudes and strategies of firms conducive to entrepreneurship, i.e. their entrepreneurial orientation (Covin & Slevin, 1989), is consistent or contradictory with philanthropy? In other words, is philanthropy strongly associated with some core components of entrepreneurship, or is it an entrepreneurial anomaly, relying on a minority of economic actors that provide important links with wider, non-economic communities. Method - The study draws on 270 randomly sampled, phone interviews with owners and ownermanagers of small and medium-sized enterprises (SMEs), i.e. firms with less than 250 employees. Interviews were conducted in Lithuania during January- March, 2008. Our results are based on confirmatory factor analysis combined with regression analysis. Results and Implications - Despite the legacy of informal institutions that is conducive neither to entrepreneurship nor to civic society, we found that by now, (i) the companies that score highest on entrepreneurial orientation construct, (ii) that perform best and those (iii) that have foreign owners are also most likely to declare their commitment to philanthropy. Our findings that most entrepreneurial firms are also involved in philanthropy are consistent with the perspective on the pattern of development in an entrepreneurial economy as outlined by Acs and Phillips (2002).

Originator or propagator? Incorporating social role theory into topic models for twitter content analysis

A large number of studies have been devoted to modeling the contents and interactions between users on Twitter. In this paper, we propose a method inspired from Social Role Theory (SRT), which assumes that a user behaves differently in different roles in the generation process of Twitter content. We consider the two most distinctive social roles on Twitter: originator and propagator, who respectively posts original messages and retweets or forwards the messages from others. In addition, we also consider role-specific social interactions, especially implicit interactions between users who share some common interests. All the above elements are integrated into a novel regularized topic model. We evaluate the proposed method on real Twitter data. The results show that our method is more effective than the existing ones which do not distinguish social roles. Copyright 2013 ACM.

Automatic labelling of topic models learned from Twitter by summarisation

Latent topics derived by topic models such as Latent Dirichlet Allocation (LDA) are the result of hidden thematic structures which provide further insights into the data. The automatic labelling of such topics derived from social media poses however new challenges since topics may characterise novel events happening in the real world. Existing automatic topic labelling approaches which depend on external knowledge sources become less applicable here since relevant articles/concepts of the extracted topics may not exist in external sources. In this paper we propose to address the problem of automatic labelling of latent topics learned from Twitter as a summarisation problem. We introduce a framework which apply summarisation algorithms to generate topic labels. These algorithms are independent of external sources and only rely on the identification of dominant terms in documents related to the latent topic. We compare the efficiency of existing state of the art summarisation algorithms. Our results suggest that summarisation algorithms generate better topic labels which capture event-related context compared to the top-n terms returned by LDA. �� 2014 Association for Computational Linguistics.

Dynamic joint sentiment-topic model

Social media data are produced continuously by a large and uncontrolled number of users. The dynamic nature of such data requires the sentiment and topic analysis model to be also dynamically updated, capturing the most recent language use of sentiments and topics in text. We propose a dynamic Joint Sentiment-Topic model (dJST) which allows the detection and tracking of views of current and recurrent interests and shifts in topic and sentiment. Both topic and sentiment dynamics are captured by assuming that the current sentiment-topic-specific word distributions are generated according to the word distributions at previous epochs. We study three different ways of accounting for such dependency information: (1) Sliding window where the current sentiment-topic word distributions are dependent on the previous sentiment-topic-specific word distributions in the last S epochs; (2) skip model where history sentiment topic word distributions are considered by skipping some epochs in between; and (3) multiscale model where previous long- and shorttimescale distributions are taken into consideration. We derive efficient online inference procedures to sequentially update the model with newly arrived data and show the effectiveness of our proposed model on the Mozilla add-on reviews crawled between 2007 and 2011. �� 2013 ACM 2157-6904/2013/12-ART5 $ 15.00.

Incorporating social role theory into topic models for social media content analysis

In this paper, we explore the idea of social role theory (SRT) and propose a novel regularized topic model which incorporates SRT into the generative process of social media content. We assume that a user can play multiple social roles, and each social role serves to fulfil different duties and is associated with a role-driven distribution over latent topics. In particular, we focus on social roles corresponding to the most common social activities on social networks. Our model is instantiated on microblogs, i.e., Twitter and community question-answering (cQA), i.e., Yahoo! Answers, where social roles on Twitter include "originators" and "propagators", and roles on cQA are "askers" and "answerers". Both explicit and implicit interactions between users are taken into account and modeled as regularization factors. To evaluate the performance of our proposed method, we have conducted extensive experiments on two Twitter datasets and two cQA datasets. Furthermore, we also consider multi-role modeling for scientific papers where an author's research expertise area is considered as a social role. A novel application of detecting users' research interests through topical keyword labeling based on the results of our multi-role model has been presented. The evaluation results have shown the feasibility and effectiveness of our model.