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More than 100 reference examples and their corresponding in-text citations are presented in the seventh edition Publication Manual . Examples of the most common works that writers cite are provided on this page; additional examples are available in the Publication Manual .
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On Thursday 6 and Friday 7 June, criminologists from across the Netherlands and Flanders descended upon the KOG Building for the sixteenth time. Leiden Law School hosted this year’s annual conference to mark the 50th anniversary of the Dutch Association for Criminology (NVC).
After the opening by the NVC’s President, Janine Janssen, the first keynote speech was given by Jan van Dijk, one of the NVC’s founders and a grey eminence of Dutch criminology. He was previously associated with Leiden University as a Professor of Criminology. In his contribution to the anniversary compilation (published by Boom), he specifically mentions his ‘Leiden mentors’, Willem Nagel and Wouter Buikhuisen.
250 conferencegoers found out more about concepts such as ‘intergenerational transmission’. This is a phenomenon regularly seen by criminologists in behaviour such as abuse and neglect, where children who have been victims of parental violence are more likely to display problematic behaviour later in life towards their own children, for example. If we extend this more broadly to criminology as a pattern of behaviour, we can also presume that future criminologists will adopt interests, research methods and other behaviour – academic or otherwise – from the criminologists who preceded them.
Besides a ‘regular’ exploration of intergenerational transmission, the keynote speeches focused on how criminology in the Netherlands and the Dutch Association have developed and what we have learned from future generations so far.
For that same reason, Leiden Law School criminologists Sigrid van Wingerden and Joni Reef set out – in their keynote – new visions for criminology teaching and education in order to prepare Leiden’s future criminologists even better.
Numerous other topics were covered in the 46 parallel sessions, ranging from experiences of imprisonment by inmates and prison guards to extremism and terrorism. The entire methodological spectrum from surveys to virtual reality also resurfaced. Leiden Law School’s criminologists were well represented during these sessions and were involved in no fewer than 43 paper presentations.
Two awards were handed out at this year’s conference. Firstly, the annual Thesis Award for the best criminology master's thesis; this year's winner is Dyon van Velzen for his thesis supervised by Leiden Law School’s Jan de Keijser. The Willem Nagel Award, which is awarded every two years for the best criminology thesis, was also part of this year's festivities. Not only was Willem Nagel one of the founding fathers of criminology at Leiden Law School, but this year’s winner is Timo Peeters, who now also works at our Criminology department here in Leiden.
Overall, we can reflect on another very successful conference and are already looking forward to next year's event, which will also take place at the KOG Building. Pencil 12 and 13 June 2025 – the provisional dates for next year’s conference – into your diary now. The provisional theme will be ‘Fake Criminology: deceptive images and false stories’. More information will be announced shortly on www.criminologie.nl, the Dutch Association for Criminology’s LinkedIn page and the Leiden Law Academy website.
CoDe, the Leiden criminology study association, played a key role in this conference by providing technical support in the lecture halls and guiding conferencegoers around the building. The conference was organised on behalf of the NVC by Leiden Law School criminologist Ilse Ras in collaboration with Leiden Law Academy.
It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .
With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business for distributed digital and AI innovation.
QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.
Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.
Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.
Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.
Let’s deliver on the promise of technology from strategy to scale.
Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.
The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.
To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.
Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.
Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.
The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.
Join our colleagues Jessica Lamb and Gayatri Shenai on April 8, as they discuss how companies can navigate the ever-changing world of gen AI.
By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.
The following are examples of new skills needed for the successful deployment of generative AI tools:
The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).
It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.
While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.
To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.
While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built. They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).
For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.
Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.
Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:
The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture are needed to maximize the future strategic benefits of gen AI:
Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.
One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.
Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.
Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.
While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.
Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.
In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.
The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.
This article was edited by Barr Seitz, an editorial director in the New York office.
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Alexander R. Goulding, M. A. Criminology Dissertation, Nottingham Trent University . ... This dissertation aims to assess normalisation of recreational drug use on an English and a Spanish sample, testing the validity of the thesis created by Parker et al. (2002). The inclusion of two different countries allows a comparative assessment of the ...
This research uses General Strain Theory (GST) (Agnew, 1992) as the theoretical framework to examine the criminal and risky behaviors of the illicit use of prescription drugs, binge drinking, and the use of illegal drugs by college students. An online survey was administered to undergraduate students at two varied campus locations.
Theses/Dissertations from 2015 PDF. Relationships Between Law Enforcement Officer-Involved Vehicle Collisions And Other Police Behaviors, John Andrew Hansen. PDF. In the Eye of the Beholder: Exploring the Dialogic Approach to Police Legitimacy, Justin Nix. PDF. Criminology on Crimes Against Humanity: A North Korean Case Study, Megan Alyssa ...
Dissertation Examples. The aim of this dissertation is to examine the claim of authors such as Harrington and Bailey (2005) that a substantial proportion of young offenders in the UK suffer from severe mental illness.... Criminology is a social science that applies elements of sociology, psychology and law in the study of crime, criminal ...
Masters and PhD. Criminology Dissertation Topic Ideas. Analyzing how marginalization and discrimination on the basis of religion contribute to crime. An examination of cybercrime laws and their role in preserving law and order. A critical examination of the United States government's response to terrorism threats.
Criminology Dissertation Example: A Case Study. Topic: The Criminology of White-Collar Crime: A Case Study of Enron Fraud . Abstract . White-collar crime, particularly corporate fraud, is a significant problem in many countries, leading to severe economic consequences and public distrust in the financial system. This research paper examines the ...
Theses/Dissertations from 1990 PDF. Claims making in the case study of missing children: A case study, James Leonard Griggs. PDF. The ignored victim: An examination of male rape in a general population, Thomas Williams . 1 2 Search. Enter search terms: Select context to search: ...
Theses/Dissertations from 2002 PDF. Role of Police, Prosecutors and Defense Attorneys in Traffic Accident Investigation and Adjudication in Chattanooga, Tennessee., Karen L. Beisel. PDF. Athletic Participation: A Test of Learning and Neutralization Theories., Mario Bernard Hankerson. Theses/Dissertations from 2001 PDF
Theses/Dissertations from 2014 PDF. Beyond Black and White: An Examination of Afrocentric Facial Features and Sex in Criminal Sentencing, Amanda Mae Petersen. Theses/Dissertations from 2013 PDF. Bringing Functional Family Probation Services to the Community: A Qualitative Case Study, Denise Lynmarie Austin. Theses/Dissertations from 2012
The Impacts of Robotics-Based Interventions on Vulnerable Youths. 18th June 2020 1. Abstract This research explores the potential impacts in terms of personal development, social bonds, employment, structure, suitability and crime reduction/prevention of a small.
Dissertations and theses from before 2014 are generally accessible only to the CUNY community, but some authors have chosen to make theirs open access. ... Elements of Social Disorganization and Environmental Criminology: A Spatial Analysis of Homicides in Villa Nueva ... Findings from a Select Sample of Low-Income Black Males in New York City ...
A Thesis Resource Guide for Criminology and Criminal Justice by Marilyn D. McShane; Frank P. Williams. Call Number: HV6024.5 .M37 2008. ISBN: 0132368951. Publication Date: 2019. This handbook is a comprehensive guide to developing and writing graduate level research. It takes the reader on a step-by-step journey through the entire thesis ...
2014. Implementation of a Randomized Controlled Trial in Ventura, California- A Body-Worn Video Camera Experiment. Download. Zimmermann, B. 2011. Educational Level of Law Enforcement Officers and Frequency of Citizen Complaints: A Systematic Review. Download. We are pleased to post a selection of theses which have been given marks of distinction.
Forensic Psychology Dissertation Ideas. A comprehensive analysis of competence to stand trial concept and its application in the UK. The age of criminal culpability: A review of the effectiveness of this idea in criminal justice. The ethics of death penalty: A review of the literature. Studying the mind of a criminal on death row: What goes in ...
For help and advice on finding suitable examples, please email: [email protected] . Albert Sloman Library - the University Library at Colchester holds print copies of all Essex Criminology M.Phil. and Ph.D. dissertations up to 30 September 2016. They are listed in the catalogue, and must be consulted in the Library. To search by department ...
Department of Law and Criminology, Edge Hill University. September 2019 . i ... Data were collected by way of semi-structured interviews, focus groups and informal discussions with two samples of participants: gang-involved or gang-associated young people, and practitioners ... dealing and provided examples of CCE. The thesis provides numerous ...
Starting Your Dissertation (Video) 46 minutes. This webinar recording will help you with the early stages of planning, researching and writing your dissertation. By the end you should be able to: --Understand the challenges and opportunities of writing a dissertation. --Move towards refining your subject and title.
This book provides a guide for undergraduate criminology and criminal justice students undertaking their final-year dissertation. It speaks to the specific challenges for criminology students who may wish to research closed institutions (such as prisons, courts, or the police) or vulnerable populations (such as people with convictions, victims ...
A literature review can be a short introductory section of a research article or a report or policy paper that focuses on recent research. Or, in the case of dissertations, theses, and review articles, it can be an extensive review of all relevant research. The format is usually a bibliographic essay; sources are briefly cited within the body ...
Succeeding with your masters dissertation : a step-by-step handbook by John Biggam This essential handbook carefully guides the student through the entire dissertation process from start to finish, offering clear, straightforward and practical advice. Biggam uses clear illustrations of what students should do- or not do - to reach their full potential, helping them to succeed with their ...
The Oxford Handbook of Criminology (6th ed.) by Alison Liebling (Editor); Shadd Maruna (Editor); Lesley McAra (Editor) Call Number: HV6025 .O87 2017. ISBN: 9780198719441. Publication Date: 2017. Chapters detail relevant theory, recent research, policy developments, and current debates.
Dissertation examples. Listed below are some of the best examples of research projects and dissertations from undergraduate and taught postgraduate students at the University of Leeds We have not been able to gather examples from all schools. The module requirements for research projects may have changed since these examples were written.
The School of Criminology and Justice Studies is proud to announce a Dissertation Proposal Defense by Cameron P. Burke entitled "Accountability, Justice, and Institutional Responses to Campus Sexual Harm." Date: Wednesday, June 12 Time: 11 a.m. to 12:30 p.m. Location: HSSB room 431 Committee
More than 100 reference examples and their corresponding in-text citations are presented in the seventh edition Publication Manual.Examples of the most common works that writers cite are provided on this page; additional examples are available in the Publication Manual.. To find the reference example you need, first select a category (e.g., periodicals) and then choose the appropriate type of ...
In this section, we'll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world. Descriptive analysis. Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical ...
Firstly, the annual Thesis Award for the best criminology master's thesis; this year's winner is Dyon van Velzen for his thesis supervised by Leiden Law School's Jan de Keijser. The Willem Nagel Award, which is awarded every two years for the best criminology thesis, was also part of this year's festivities.
It's time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI's enormous potential value is harder than expected.. With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI ...