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Introduction: The ethical implications of deoxyribonucleic acid (DNA) profiling or DNA fingerprinting or forensic genetics in criminal investigations have gained significant attention worldwide. In India, DNA profiling in criminal investigations has increased over the years. However, the ethical considerations of DNA profiling in India have yet to be examined adequately.

Purpose: The study aimed to examine the ethical considerations of DNA profiling in India and compare them with international guidelines. By examining the ethical considerations of DNA profiling in India, this study seeks to contribute to the ongoing discourse on the responsible use of DNA profiling in forensic investigations.

Methodology: The study used a qualitative research design, and data were collected by reviewing relevant literature and laws.

Findings: The findings indicate that the Indian legal framework has gaps in addressing the ethical considerations raised by international guidelines, such as the admissibility of DNA evidence in court, oversight of DNA laboratories, safeguards against discrimination, and privacy and confidentiality protections.

The comparative analysis highlights the need for strengthening the legal framework in India, adopting best practices from international guidelines, and incorporating safeguards to protect against discrimination and ensure the privacy and confidentiality of individuals. By adopting these recommendations, India can ensure that DNA profiling is conducted ethically and responsibly, promoting public trust in the criminal justice system and upholding the rights of all individuals.

All societies in the modern world are troubled by crime, and the general public is equally fascinated by criminals and fearful of criminal behaviour. In the United Kingdom, events such as the murders of Jack the Ripper, the Yorkshire Ripper and Harold Shipman, and the Soham tragedy, coupled with film and television programmes including Silence of the Lambs, Cracker and Crime Scene Investigation, have fuelled the public's consciousness of the criminal mind.In the fight against crime, the development of offender profiling by the FBI in the USA has further captured people's imagination. The technique was introduced to help law enforcement agencies solve serious crimes such as serial rape or murder, and to a lesser extent arson and property crime. At the heart of profiling lies the belief that by combining psychological principles with crime scene analysis, it is possible to identify the likely characteristics of a perpetrator.Although advances in crime detection are welcomed, the profiling field appears riddled with contradiction and disagreement. Social scientists argue that the discipline is unscientific due to methodologically weak research, while police officers appear sceptical about its benefits for solving crime. In Britain, profiling has witnessed both notable successes, for example Canter's profile of the serial rapist and murderer John Duffy, and dramatic failures, such as the Colin Stagg profile in the Rachel Nickell inquiry. This article reviews the offender profiling literature, examines its applicability in the legal system and identifies areas for future research.

This paper examines the relationship between linguistic profiling and English language teachers’ career development.

This paper collected data from 20 participants using a qualitative approach. Semi-structured interview guides were used to collect qualitative data on this topic.

After collecting and analyzing the data, the results showed that linguistic profiling results in demotivation and low self-esteem and can spur career development among non-native English teachers.

This paper advances scientific knowledge by providing empirical evidence showing that while linguistic profiling has some negative influences, it can spur career development among non-native English teachers.

The purpose of this paper is to develop and investigate the idea that self-profiling and career control by temporary agency workers (TAWs) in low-skill jobs are positively related to informal learning and that this relationship is mediated by job challenge.

An online survey study was conducted among 722 TAWs in low-skill jobs in the Netherlands. Bootstrap mediation analyses were used to test the hypotheses.

Self-profiling and career control are positively related to informal learning of TAWs and these relationships are mediated by job challenge.

This is the first study to develop and empirically test the proposition that self-profiling and career control are important factors for enhancing employees’ learning experiences in low-skill jobs.

Hiring companies and temporary work agencies could stimulate and train TAWs’ self-profiling and career control competencies to enhance their job challenge and informal learning. Organizations should consider assigning challenging tasks to TAWs, which may be a good alternative for expensive formal training programs.

Many TAWs in low-skill jobs do not possess the skills and capacities to obtain a better or more secure job. In general, temporary workers face a higher risk of unemployment and greater income volatility (Segal and Sullivan, 1997). Gaining knowledge about how to develop this group is important for society as a whole.

Research on the determinants of informal learning mainly concerned higher-educated employees and managers with long-term contracts (e.g. Dong et al., 2014), whereas very little is known about factors that stimulate informal learning among TAWs in general, and among TAWs in low-skill jobs in particular.

The purpose of this paper is to conduct an independent analysis of all existing geographic profiling software packages to determine if any one is more accurate than the others or if any of the software systems are any more accurate than simple spatial distribution strategies at locating the home base of serial offenders.

An analysis was conducted of all existing geographic profiling software as well as three spatial distribution methods of profiling. Differences in accuracy were assessed using four different methods; dichotomous profile accuracy, simple error measurement, profile error distance, and average top profile area.

Results indicate that not only are the different profiling software systems no more accurate than the spatial distribution control methods, but that accuracy in general was marginal at best. In addition results indicated that certain crimes, such as commercial robbery, were particularly difficult to profile and that the number of crimes in a series was not by itself a good indicator of success of a profile.

The paper shows that future research needs to focus more on determining how various factors such as city type, crime type, road network and spatial aspects of a crime series (dispersion and search area) impact profiling accuracy. In addition future research should also endeavor to determine whether these advanced strategies are substantially more accurate than other simple profiling strategies such as human prediction. Finally, future research should also seek to examine geographic profiling in a real world setting and how geographic profiling impacts the success of open investigations.

Practically, this study casts doubt not only on the overall accuracy of profiling strategies in predicting the likely home location of an offender, but also on whether probability strategies are substantially better than spatial distribution strategies.

This research was the first to independently analyze all of the existing geographic profiling systems against control methods for the purpose of determining the accuracy of these different methods.

This article examines the overlooked literature on algorithmic profiling in public employment services (APPES) in the field of public administration. More specifically, it aims to provide an overview and connections to identify directions for future research.

To understand the existing literature, this article conducts the first systematic literature review on APPES. Through inductive coding of the identified studies, the analysis identifies concepts and themes, and the relationships among them.

The literature review shows that APPES constitutes an emerging field of research encompassed by four strands and associated research disciplines. Further, the data analysis identifies 23 second-order themes, five dimensions and ten interrelationships, thus suggesting that the practices and effects of algorithmic profiling are multidimensional and dynamic.

The findings demonstrate the importance of future research on APPES undertaking a holistic approach. Studying certain dimensions and interrelationships in isolation risks overlooking mutually vital aspects, resulting in findings of limited relevance. A holistic approach entails considering both the technical and social effects of APPES.

This literature review contributes by connecting the existing literature across different research approaches and disciplines.

Expert profiling plays an important role in expert finding for collaborative innovation in research social networking platforms. Dynamic changes in scientific knowledge have posed significant challenges on expert profiling. Current approaches mostly rely on knowledge of other experts, contents of static web pages or their behavior and thus overlook the insight of big social data generated through crowdsourcing in research social networks and scientific data sources. In light of this deficiency, this research proposes a big data-based approach that harnesses collective intelligence of crowd in (research) social networking platforms and scientific databases for expert profiling.

A big data analytics approach which uses crowdsourcing is designed and developed for expert profiling. The proposed approach interconnects big data sources covering publication data, project data and data from social networks (i.e. posts, updates and endorsements collected through the crowdsourcing). Large volume of structured data representing scientific knowledge is available in Web of Science, Scopus, CNKI and ACM digital library; they are considered as publication data in this research context. Project data are located at the databases hosted by funding agencies. The authors follow the Map-Reduce strategy to extract real-time data from all these sources. Two main steps, features mining and profile consolidation (the details of which are outlined in the manuscript), are followed to generate comprehensive user profiles. The major tasks included in features mining are processing of big data sources to extract representational features of profiles, entity-profile generation and social-profile generation through crowd-opinion mining. At the profile consolidation, two profiles, namely, entity-profile and social-profile, are conflated.

(1) The integration of crowdsourcing techniques with big research data analytics has improved high graded relevance of the constructed profiles. (2) A system to construct experts’ profiles based on proposed methods has been incorporated into an operational system called ScholarMate (www.scholarmate.com).

One shortcoming is currently we have conducted experiments using sampling strategy. In the future we will perform controlled experiments of large scale and field tests to validate and comprehensively evaluate our design artifacts.

The business implication of this research work is that the developed methods and the system can be applied to streamline human capital management in organizations.

The proposed approach interconnects opinions of crowds on one’s expertise with corresponding expertise demonstrated in scientific knowledge bases to construct comprehensive profiles. This is a novel approach which alleviates problems associated with existing methods. The authors’ team has developed an expert profiling system operational in ScholarMate research social network (www.scholarmate.com), which is a professional research social network that connects people to research with the aim of “innovating smarter” and was launched in 2007.

Building energy performance is a key element when assessing the sustainability, or otherwise, of the built environment. However, the prescriptive nature of current approaches to sustainability assessment stultifies innovative approaches to sustainable building design. The purpose of this paper is to illustrate how a current EU FP7‐funded project, entitled “IntUBE – Intelligent Use of Buildings' Energy Information,” will contribute to improving the measurement and evaluation of building energy performance. The paper also seeks to highlight the potential offered by the energy‐profiling tools and techniques being developed as part of the project to contribute to the assessment of sustainable urban development.

The IntUBE project is developing new methods of integrating the information and communication technologies (ICTs) used in the design and operation of buildings, with the aim of facilitating improvements in the energy performance of buildings and the measurement of that performance. To do so, the project will develop new methods and tools which integrate design phase and operational phase energy profiling.

Integrating the ICTs used to assess the design and operation of buildings could significantly improve the way in which the energy performance of buildings is assessed. However, there are many barriers to the adoption of the energy‐profiling software tools designed for professional use. This is problematic because a more generalised uptake of these tools is essential to improving the measurement and evaluation of building energy performance.

It is important that the energy‐profiling tools and techniques developed within the IntUBE project ameliorate as many of the barriers to the adoption of the energy‐profiling software tools as possible. To do so new approaches to the following are under development; the integration of building information model authoring software and energy simulation software, the integration of simulation and real‐time data‐capturing sensors, and evaluating and selecting amongst the design alternatives offered by energy simulation software.

User profiling with big data raises significant issues regarding privacy. Privacy studies typically focus on individual privacy; however, in the era of big data analytics, users are also targeted as members of specific groups, thus challenging their collective privacy with unidentified implications. Overall, this paper aims to argue that in the age of big data, there is a need to consider the collective aspects of privacy as well and to develop new ways of calculating privacy risks and identify privacy threats that emerge.

Focusing on a collective level, the authors conducted an extensive literature review related to information privacy and concepts of social identity. They also examined numerous automated data-driven profiling techniques analyzing at the same time the involved privacy issues for groups.

This paper identifies privacy threats for collective entities that stem from data-driven profiling, and it argues that privacy-preserving mechanisms are required to protect the privacy interests of groups as entities, independently of the interests of their individual members. Moreover, this paper concludes that collective privacy threats may be different from threats for individuals when they are not members of a group.

Although research evidence indicates that in the age of big data privacy as a collective issue is becoming increasingly important, the pluralist character of privacy has not yet been adequately explored. This paper contributes to filling this gap and provides new insights with regard to threats for group privacy and their impact on collective entities and society.

The purpose of this article is to improve the use of evidence-based practice and research utilization in the offender profiling process. The use of offender profiling has been met with increasing resistance given its exaggerated accuracy. The “Investigative Journalist/Expert Field Micro Task Force” model, a collaborative method that incorporates offender profiling and is designed to address unresolved serial homicides, is introduced and evaluated alongside recommendations on attaining adherence.

The model was field tested in 17 instances. The measures used by the Federal Bureau of Investigation to gauge the usefulness of their case consultations, whether their input helped catch the offender, offer new leads, move the case forward, provide new avenues or give new ideas, were used to evaluate the model.

The model established likely patterns of serial murder activity among strangulations of women in Chicago, Cleveland, and Panama and resulted in convictions of suspects in Louisiana and Kansas City. This model is valuable when used to parse modern-day offenders from those who committed unresolved homicides as the latter display different behaviors that can make investigations difficult endeavors. Results from the field tests mirror those from the literature in that profiling alone did not result in the capture of serial killers. Instead, profiling was used in conjunction with other efforts and mainly as a means to keep the investigation moving forward.

Unresolved homicides are at a point of crisis and represent a significant but largely unaddressed societal problem. The success of this model may compel law enforcement to restore faith in offender profiling.