Abstract

In private capital investment, limited partners (LPs) and general partners (GPs) frequently encounter the challenge of finding suitable counterparts amid limited information, a process often hindered by market inefficiencies. This article addresses this issue by exploring the micro-level mechanisms that shape private capital networks, employing temporal exponential random graph models. Our findings uncover activity and popularity effects, persistence mechanisms, and homophily in preferences concerning region, strategy, and industry. These factors jointly shape the dynamically evolving network structure across asset classes and the hybrid network with all asset classes, revealing a shared network formation process. This article offers practical insights into the matching problem within the private capital market.

Read the article in 

Flow matching is a generative modelling technique that learns to transform random noise into meaningful data by following smooth trajectories. Think of it as a more flexible and efficient cousin of diffusion models. This approach has become a backbone for many modern generative models across different domains - from image generation to, in this case, synthetic tabular data.

The collaborative environment at AMLab fostered innovative discussions that refined the methodological framework and strengthened the international research network between the Department of Social Statistics and the University of Amsterdam. Working at one of Europe's leading machine learning research labs provided a unique environment to engage with cutting-edge computational and quantitative research, especially in synthetic data generation.

The findings from this work are now available as a preprint on , representing a significant milestone in advancing generative models for tabular data, especially to provide privacy-preserving and high-quality synthetic data. This collaboration exemplifies the Department's commitment to fostering global academic partnerships and contributing methodological advancements to the broader machine learning and statistical community.

Isabella's master's thesis from Queen鈥檚 University in Canada focused on how peer support levels and problematic social media use of youth in military families differed from non-military-connected youth. This project used the largest Canadian dataset with responses from military-connected youth themselves, the HBSC survey. Youth in military families have unique experiences that can shape their health outcomes, which is why this research is important. 

Isabella was awarded the Colonel Russell Mann Military, Veteran and Public Safety Families Research Award for my research 鈥淥nline communication and problematic social media use among military-connected youth in Canada鈥�. This award recognises high-quality Canadian research that deepens the understanding of the health, resilience and lived experiences of military, Veteran and public safety families.

The focus of the visit was to contribute to the development of a novel methodological framework that integrates Matrix Decomposition-based (MD) estimation into Structural Equation Model (SEM) Trees and Forests.

Traditional SEM Trees rely on Maximum Likelihood Estimation (MLE), which can be unstable, have improper solutions (e.g., negative variances), and be computationally intensive, especially in small subgroups or misspecified models. The team in Tokyo worked on developing an alternative approach based on matrix decomposition, which avoids many of the pitfalls of likelihood-based estimation.

During his visit, Ahmed worked on extending existing simulation studies from single-tree models to ensemble-level forest models, comparing four major estimation frameworks: MD-based SEMTree, Maximum Likelihood-based, constrained ML-based, and Bayesian SEMTree. He implemented forest-level simulations that assess both shared metrics (such as improper solution rates, computational time, and node recovery) and forest-specific ones (like variable importance, prediction accuracy, and ensemble diversity). He also continues to explore alternative splitting algorithms, including Factor Analysis by Instrumental Variables (FABIN) and other non-iterative multi-start approaches. Eventually, the team intends to develop an open-source R packages to support this new methodology.

This visit provided a unique environment to engage with cutting-edge computational and quantitative research which contributes to the methodological advancements that will benefit the broader SEM and statistical community.

Ahmed will continue to collaborate with Professor Usami and Dr Todo not only to finalize and publish this research, but to collaborate for more research together in the future. The Department of Social Statistics at the University of 黑料网吃瓜爆料 and the team at University of Tokyo both expressed interest in more contact and collaboration in the future between the departments and the universities in general.

Examining how young people across Europe imagine and plan their path to adulthood, the article, 鈥�,鈥� draws on data from the Generations and Gender Survey and the European Social Survey to explore young adults鈥� ideal ages, intentions, and actual behaviours across 33 European countries. 

The study provides a comparative picture of when young people expect to leave home, form partnerships, marry, and become parents; and how these expectations align, or fail to align, with reality.

Findings show that while young Europeans tend to view their twenties as the ideal period for key life transitions, they often experience these events later than intended. The mismatch between ideals and outcomes highlights persistent gender and regional differences, suggesting that cultural norms and structural barriers continue to shape the pathways to adulthood across Europe.

Primary health services are the first point of contact for those seeking support for common mental health problems, such as anxiety and depression. These services form an essential part of the response to the UK鈥檚 mental health crisis. 

Engagement with those accessing and navigating primary care pathways for common mental health problems has highlighted that accessing and engaging with support frequently involves struggling with complex and burdensome bureaucratic processes at an already difficult time. These experiences can leave people with a profound sense of unjust treatment that continues throughout their journey through the system.

Research across different disciplines has suggested that perceptions of 鈥榡ust鈥� treatment can also significantly impact treatment outcomes by affecting engagement with services, deterring individuals from seeking support in the future, and exacerbating existing inequalities within the system.

A research programme, led by Professor Joe Tomlinson and funded by a 拢2.5 million Discovery Award, will pioneer the application of the theory of 鈥榖ureaucratic justice鈥� in primary mental health services. By deploying an innovative combination of a longitudinal quantitative and qualitative study with participatory storytelling methods, the programme will develop a new understanding of the nature and relevance of just treatment in the context of how people interact with frontline administrative processes in primary care mental health services.

The programme will also build an interdisciplinary community of researchers, while providing a platform to embed lived experiences of seeking mental health support into research practices in innovative and engaging ways.

The programme formally launches in January 2026 and will run for six years. Alongside Professor Joe Tomlinson, The Dickson Poon School of Law and King鈥檚 College London, the programme will benefit from the expertise of co-investigator Head of the and Deputy Director of the at 黑料网吃瓜爆料. Co-investigators are also based at the University of York (including Dr Jed Meers, Dr Simona Manni, Dr Annie Irvine, Dr Aisling Ryan, and Professor Lina Gega).

The research, funded by Good Neighbors (in 2022-23) and 黑料网吃瓜爆料 - International Science Partnerships Fund (in 2024-25), was led by a joint team from the School of Social Sciences and , University of 黑料网吃瓜爆料, including Dr Jihye Kim, Professor Wendy Olsen, Dr Mohammed Ibrahim, Harshada Ambekar, Sonny McCann and Mindy Park. 

They conducted both surveys and focus groups, finding that students who participated in the programme showed a shift in their attitude about early pregnancy, such as postponing the expected age of having their first child. The programme鈥檚 success is attributed to its emphasis on education, future development, and mutual respect among peers, rather than solely focusing on negative outcomes.

Community leaders have observed a . This progress is encouraging, but the report emphasises the need for continued support and a long-term strategy. Misunderstandings about family planning still need to be addressed at a community level to achieve a sustainable reduction in teenage pregnancy.

The study concludes that social engagement is a potent tool for empowering young people and improving sexual and reproductive health knowledge.

The baseline and follow-up study reports are available on the . The paper on the results from the baseline study has been released in .

We gratefully acknowledge the support of the Good Neighbors Alliance, known globally as Good Neighbors International, a federation of independent but affiliated Non-Government Organisations operating in over 50 countries.

You can read the paper, 鈥溾��, online.

Political elites in the US are ideologically divided over climate change. We identify two perspectives: 

• The intrinsic view on ideological climate polarisation views climate beliefs as entrenched parts of an actor鈥檚 identity and posits that ideological positions have factually shifted towards ever more extreme positions over time.
• The instrumental view, in contrast, emphasises that polarisation entrepreneurs mobilise their constituency to participate in the climate policy debate by amplifying ideological differences over climate-related focusing events when they arise, leading to fluctuations in visible polarisation, rather than a steady trend. 

This study examines which of the two perspectives holds in US Congressional and subnational media debates by analysing time trends of polarisation and phases of structural stability. We distinguish between endogenous events, which can be attributed to the political process, and exogenous focusing events, such as extreme events or those related to the international climate regime, and investigate which type of event tends to be associated with changes in polarisation. 

Applying two novel time series measures for discourse networks - structural polarisation and the detection of phases of structural stability - to the climate debate during the 112th to 114th Congress (2013鈥�2017) and subnational print media in four swing states, we find that exogenous events are largely irrelevant while endogenous political dynamics increase the polarisation of the debate considerably. 

We find ups and downs of polarisation corresponding to distinct structural phases in which polarisation is linked to participation. This temporal fluctuation of polarisation around endogenous political events is consistent with the instrumental perspective.

Drawing on their expertise in population data, large scale social surveys, and the use of data to inform fair and effective health policy, the 黑料网吃瓜爆料 academics highlight the vital role of robust evidence in ensuring policy keeps pace with societal change.

Launched in 1991, the Health Survey for England, has been the backbone of health policy evidence for more than three decades, providing annual, nationally representative data to monitor the nation鈥檚 health and guide healthcare delivery. At the June 2025 conference, the government confirmed that NHS England would no longer run the survey. While details of any replacement are yet to be confirmed, discussions are under way on the future of population health surveys in England.

Since the conference, The UK Data Service has acted swiftly to ensure researchers鈥� voices are heard in the national debate:

• Boosting consultation responses 鈥� negotiating with (DHSC) and the (ONS) to re-open a user consultation on the future of population health surveys for three weeks in July, generating a further 140 responses from health survey users.
• Showcasing impact 鈥� collaborating with a key health survey user to publish a Data Impact blog highlighting the importance of the survey: .
• Supporting decision-makers 鈥� at DHSC/ONS鈥檚 request, preparing a written summary of the Health Survey for England鈥檚 value and impact, including user testimonies, to feed into their internal report (due end of September), and advising on an expert peer-reviewers.

Through this work, 黑料网吃瓜爆料 academics and the UK Data Service are playing a pivotal role in shaping the future of the Health Survey for England, influencing how health data will be collected to meet the challenges of a changing world.

Practical sessions included the implementation of Cox Proportional Hazards models and Kaplan-Meier estimators using R. A highlight was a constructive meeting with Professor Yabiku and colleagues concerning future work and collaboration on research on migration.

KOSTAT-UNFPA Summer Seminar on Population

The Summer Seminar on Population was first launched by the East-West Center (EWC) in 1970 and quickly gained recognition as a leading population seminar series. In 2013, responsibility for population activities was transferred to Statistics Korea (KOSTAT), which hosted the first KOSTAT Summer Seminar on Population in 2014.

Since 2017, the seminar has been co-hosted annually by KOSTAT and the United Nations Population Fund (UNFPA), continuing its mission to advance population studies and statistical capacity building.

The seminar serves as an international platform for government statisticians, graduate students, and population experts to exchange ideas, share research, and strengthen statistical capacity in population-related fields. Over the years, participants from more than 20 countries have taken part in the programme.

This study examines the heterogeneous labour market effects of family leave policies for single and partnered mothers. 

Longer family leave has been shown to weaken women鈥檚 labour market positions and some studies have found heterogeneous effects across population groups. However, whether the effect differs by partnership status remains unexplored. 

Using Finnish register data from 1989 to 2014 (ca. 2.5 million person-years) and controlling for selection into single motherhood by comparing estimates from OLS and FE models, this study compares single and partnered mothers鈥� unemployment and earnings consequent to extended family leaves. In line with predictions that single mothers may face greater work-family reconciliation issues or cumulative disadvantage leading to greater labour market penalties, the results showed that longer leave increases the length of unemployment for single mothers more than for partnered ones. 

This is not solely because of selection into single motherhood. Earnings penalties after family leave (net of employment status) are the same for single and partnered mothers. 

We conclude that similar long- lengths of family leave are penalised more among single mothers in terms of employment, which increases and reproduces social inequalities. This means that existing inequalities are reinforced by labour market absences supported by leave policies.

The ESRA Outstanding Service Award acknowledges sustained and high-level contributions to European survey research, either of a methodological, substantive or infrastructural nature.  Nominations are made and voted on by members of the ESRA Committee.

Natalie publishes widely in areas of survey statistics and survey methodology, including survey design and estimation, adaptive survey designs, small area estimation, non-probability sampling, data linkage and integration, confidentiality and privacy. 

She is an elected member of the International Statistical Institute (ISI), a fellow of the Royal Statistical Society, a fellow of the Academy of Social Sciences and President 2023-2025 of the International Association of Survey Statisticians.  She also serves on editorial boards and international Methodology Advisory Boards at National Statistical Institutes.

In her acceptance speech for the award, Natalie noted that survey methodology and survey statistics are becoming increasingly important. She emphasised the need for high-quality randomised probability-based survey data to evaluate accuracy and mitigate biases in non-survey data sources, such as administrative data, big data and non-probability samples. She mentioned that only through the knowledge and understanding of the theoretical underpinnings of statistical methods and inference can we truly move forward into the digital and AI era.

Policy subsystems are comprised of competing advocacy coalitions, in which public and private political actors with shared belief systems learn from each other and coordinate their strategies in the pursuit of influencing policy making in their favour. 

While numerous studies have focused on the longevity and structural stability of advocacy coalitions, there is scant theory and evidence on how nascent policy subsystems bifurcate into stable, competing coalitions. 

This article proposes a three-stage model of problem discovery, differentiation, and consolidation. 

We apply discourse network analysis to the nascent subsystem of the UK's COVID-19 response in order to study these phases and discuss their applicability and implications for other institutional and issue contexts.

Our colleagues, Eduardo F茅 & Mario Pezzino, have published a study in Decisions in Economics and Finance. Read the paper: .

Co-creation - where students help design teaching materials - has clear short-term benefits for engagement and soft skill development. But our new research shows it also creates powerful intertemporal peer effects: students exposed to co-created materials become more motivated, feel part of a learning community, and are more likely to co-create themselves.

We develop a dynamic model of how co-created resources influence student effort over time and test this through a behavioral experiment in an intermediate microeconomics course. The results suggest that co-creation not only deepens learning, but can gradually reshape education culture鈥攂oosting what we call "education morale."

Findings

• Co-creation promotes responsibility, reciprocity, and intrinsic motivation
• Exposure to peer-created materials increases future engagement
• Even small interventions (e.g., emails from former students) can create measurable, lasting effects.

The paper 鈥淕oodbye human annotators? Content analysis of social policy debates using ChatGPT鈥� can be .

Content analysis is a valuable tool for analysing policy discourse, but annotation by humans is costly and time consuming. ChatGPT is a potentially valuable tool to partially automate content analysis for policy debates, largely replacing human annotators. 

We evaluate ChatGPT鈥檚 ability to classify documents using pre-defined argument descriptions, comparing its performance with human annotators for two policy debates: the Universal Basic Income debate on Dutch Twitter (2014鈥�2016) and the pension reforms debate in German newspapers (1993鈥�2001). We use the API (GPT-4 Turbo) and user interface version (GPT-4) and evaluate multiple performance metrics (accuracy, precision and recall). 

ChatGPT is highly reliable and accurate in classifying pre-defined arguments across datasets. However, precision and recall are much lower, and vary strongly between arguments. These results hold for both datasets, despite differences in language and media type. Moreover, the cut-off method proposed in this paper may aid researchers in navigating the trade-off between detection and noise. 

Overall, we do not (yet) recommend a blind application of ChatGPT to classify arguments in policy debates. Those interested in adopting this tool should manually validate bot classifications before using them in further analyses. At least for now, human annotators are here to stay.

The paper 鈥淲hen does discursive change happen? Detecting phase transitions in discourse networks of sustainability transitions鈥� can be found

Sustainability Transitions Research (STR) confronts complex societal challenges by examining societal shifts and their trajectories. An emerging perspective in STR is discursive approaches, which analyse the role of discourses and discourse coalitions in shaping sustainability transitions. However, discursive approaches face challenges regarding the analysis of sustainability transition processes as complex, temporal processes of stability and change. 

We discuss the nature of these challenges and extend the method of discourse network analysis (DNA) by measuring distinct temporal states (phases of stability) in discourse networks and detecting phase transitions (significant changes) between these discursive states. 

Whereas most approaches analyse discursive changes in a top-down way, we introduce a method for the bottom-up detection of discursive stability and change. This facilitates a more accurate tracing of how sustainability transitions unfold over time. An empirical application of this extension to the discursive networks around the introduction of a Low Emission Zone demonstrates how and when discourses and actors display significant structural shifts. This methodological innovation addresses the need for measuring stability and change in the complex, discursive, temporal dynamics of sustainability transitions.

Highlights

• Identification of 3 challenges of discursive approaches to energy transitions.
• Discourse Network Analysis is extended by drawing on mathematical network science.
• Bottom-up detection of temporal phases, states, transitions in discourse networks.
• Energy transitions should be analysed through the lens of temporal networks.
• Case study on the changing debate on a low emission zone in Ghent, Belgium.

You can read the paper, 鈥淢ultiregional Population Forecasting: A Unifying Probabilistic Approach for Modelling the Components of Change鈥�, on.

Regional population forecasts are important for planning and understanding how populations are changing and redistributing. To forecast regional population changes, one must have a mechanism to capture different sources of population growth. In low fertility and developed societies, the main factors driving population redistribution are internal migration and immigration, for which both tend to concentrate people towards large metropolitan areas. 

In this article, we extend the multiregional cohort-component population projection model developed by Andrei Rogers and colleagues in the 1960s and 1970s to be fully probabilistic, by using Bayesian inference. We apply the model to forecast population for eight states and territories in Australia. 

The projections are based on forecasts of age-, sex- and region-specific fertility, mortality, interregional migration, immigration and emigration. The approach is unified by forecasting each demographic component of change by using a combination of log-linear models with bilinear terms. 

This research contributes to the literature by providing a flexible statistical modelling framework capable of incorporating the high dimensionality of the demographic components over time.

Forecasts of a population totals by sex for states and territories in Australia, b total population. States or territories: NSW鈥擭ew South Wales, VIC鈥擵ictoria, QLD鈥擰ueensland, SA鈥擲outh Australia, WA鈥擶estern Australia, TAS鈥擳asmania, NT鈥擭orthern Territory, ACT鈥擜ustralian Capital Territory. 

Prior to starting the position, Mariana was working on her PhD in Sociology at the University of Oxford. Her PhD was titled 鈥溾�� and explores only-child fertility from a sociodemographic perspective. 

At the University of Oxford she also worked as a research assistant on the project 鈥淒igital Gender Gaps鈥�, focusing on combining traditional surveys and big data for population analysis. Her main research interests are family demography, fertility and the use of formal demographic methods for exploring changes in kinship networks.

Since joining 黑料网吃瓜爆料, she has been co-teaching in SOST10012-Understanding Social Media Data and SOST2002-Essentials of Survey Design and Analysis.

Prior to moving to the UK, Mariana trained as a demographer at the Centre for Development and Regional Planning (Cedeplar) in Brazil, where she was awarded an MSc, and developed her research interests in family demography, as well as formal demographic methods.

The award is presented annually to students who demonstrate exceptional contributions to artificial intelligence (AI) and data science research.

's doctoral research employs advanced machine learning techniques to examine the causal impact of front-of-package food labelling on consumer behaviour, drawing on both randomised trials and observational data. Her research in this field has implications in areas ranging from health causal inference to data linkage.

She will receive funding to continue the research and join the Turing Institute network, a collaborative platform aimed at advancing AI and data science.

Congratulations to Constanza on this remarkable achievement!

Further information on the Turing Enrichment Scheme can be found on .

鈥溾�� is available to read online.

Political elites express their ideological positions on contentious issues across various arenas in the public sphere. Social science research often relies on data extracted from various media or political and administrative sources, as well as surveys that are administered directly with the political actor. 

Although some studies compare ideology across different sources, few systematically analyse how political actors adjust their ideological messaging to the audiences in the respective communication arenas and how such changes are associated with systematic bias in data sources. 

This paper uses a unique dataset, combining climate policy belief observations from three arenas - social media, Congressional testimony, and surveys - on identical ideological variables and during the same time period. 

We apply item response theory to understand how responses differ by arena and find that ideological communication on X is most left-leaning, Congressional testimony is most right-leaning, and surveys, the data source with the smallest potential arena effect, is in the middle. We also find that actors with strong ideological leaning moderate their positions on social media and in Congress. 

These findings enhance our understanding of strategic communication depending on audience context and inform social research on biases when analyzing specific data sources.

I am a Professor of Social Statistics in the at 黑料网吃瓜爆料, which is part of the . I am also a member of the and the . 

Before joining 黑料网吃瓜爆料 in April 2024, I was a Professor of Comparative Politics in the Department of Government at the University of Essex. 

I am also a DFG Mercator Fellow in the Research Training Group on Digital Platform Ecosystems at the University of Passau (2022-2027) and serve as chair of the Political Networks Section of the American Political Science Association (APSA) in 2024-2025. 

My main research interests are politics and public policy, network analysis and complex systems, statistical modelling, and computational social sciences. I am best known for my work on discourse network analysis (and the software ), the R package, and my work on statistical models for longitudinal network data (e.g., implemented in the R package). 

My research has appeared in leading political science journals (e.g., AJPS, JOP, BJPS), public policy and administration journals (e.g., PSJ, JPART), technical journals (e.g., Physica A: Statistical Mechanics, Network Science, JStatSoft), and outlets in other fields (e.g., Nature Climate Change, Addiction, Personality and Social Psychology Bulletin). 

More information on Professor Philip Leifeld can be found on . 

The paper, 鈥淟earnings from a decade of data fellows: Co-creation of a data skills framework for non-stem students鈥�, is available to . 

This workshop provided a reflection on an experiential learning model developed in the UK. The Data Fellows initiative supports undergraduate social science and humanities students to develop their data skills through work placements. 

The findings have resulted in a book, academic articles and international presentations which collectively provide a substantial body of evidence to illustrate how non-STEM (science, technology, engineering and maths) students can learn and practice their data analytic skills and progress into data and technical careers. 25% of the 373 Data Fellows placed to date have been from historically under-represented groups and 70% have been female. 

A case study was presented to show a journey from a first degree in social science to a postgraduate degree in data science. The aim of the workshop was to challenge the deficit narrative that can accompany the teaching of data skills in the social sciences and explore whether a suitable data skills framework exists or could be developed.

For more information about Jackie visit .

Slides and recordings (if available) from these events can be accessed on the event web page by clicking the links above. Recordings are also available on the . 

For information about upcoming events visit the .
 

Our colleague, , has recently published a study, , in Environmental Communication. 

The authors propose an account of environmental communication as a dynamic space involving multiple expert knowledges to address growing diversity of expert knowledges in environmental communication. 

To enable this account, they offer a computer-assisted mapping technique relating these knowledges to each other at various time points. 

The authors illustrate the proposed approach with a case study on flood risk management in the UK, where diverse expert groups have been engaged in a shared communication space which enabled coordination of their knowledges over time. 

They conclude that researchers can use the proposed technique to trace knowledge dynamics in environmental communication. Communication practitioners can use it to map thematic areas that experts specialise in, identify knowledge gaps, find relevant documents, and facilitate expert communication. 

The project 鈥淏uilding computational capacity among global data service staff鈥� was funded for the UKRI's call on 鈥�."

Digital research infrastructures connect researchers, policymakers and innovators with the computers, data, tools techniques and skills to undertake ambitious and creative research. 

Social science data services worldwide play a key role in the digital research infrastructure by curating and managing access to many forms of social and economic data as well as promoting increased data literacy among the community. 

Recognising the growing importance of computational skills for data services staff in the social sciences, this project, led by academics affiliated with the UKDS, will address the critical need for training. 

The aim of this project is to build capacity within the international data services community, by providing upskilling opportunities for UK Data Service (UKDS) staff and developing foundational level data skills modules in computational social science for the wider global community. It will also establish a community of practice to provide enhanced support to users through the lifetime of the project and beyond. 

Direct beneficiaries of this project will include UKDS staff who will be given the opportunity to upskill in computational skills, as well as global data service staff who will be given access to a foundational-level online structured course(s) on computational social sciences. 

Through both upskilling mechanisms, this project will enhance data services capacity both in the UK and globally, enhance the careers of data service professionals, and through the establishment of a Community of Practice will contribute to a culture of lifelong learning.

Attendees explored how sociotechnical changes might shape the future of research, hearing from a variety of experts and collaborating with researchers and professionals from different sectors and disciplines. 

In addition to fostering vibrant discussions, Methods Con: Futures 2024 featured the launch of NCRM鈥檚 a series of publications reflecting on how sociotechnical changes might impact the way social science is conducted. 

The event was a resounding success, garnering excellent feedback for its interdisciplinary approach, valuable insights and providing an opportunity to engage with cutting-edge methods. 

The NCRM is proud to have facilitated such a dynamic exchange of ideas and looks forward to its continuing commitment to staying at the forefront of methodological innovation in the social sciences. 

During the month of September, University of 黑料网吃瓜爆料 colleagues at the UK Data Service (UKDS) delivered the following events: 

• (10 September, J. Kasmire, Louise Capener and Nadia Kennar) 
• (24 September, Nigel Denoronha and Placide Abasabanye) 
• (24 September, Alle Bloom, Pierre Walthery) 
• Presentation at RSS International Conference 2024: Coding in public: recognising vulnerability as a barrier to reproducibility (2 September, J. Kasmire) 
• Presentation at RSS International Conference 2024: Developing quantitative data driven research skills in social science students outside the classroom (2 September, Vanessa Higgins) 
• Presentation at NCRM methods showcase: The UK Data Service Data Skills Framework (11 September, Vanessa Higgins)

Slides and recordings (if available) from these events can be accessed on the event web page by clicking the links above. Recordings are also available on the .

For information about upcoming events visit the . 

The Department of celebrates the publication of the report 鈥淓xploring the Intersection of Technology and Democracy鈥�. 

Led by Professor , the 2023 Futures Summer Camp, funded by SPRITE+, was a collaborative effort with the Research Institute for Sociotechnical Cyber Security (RISCS) and facilitated by the School of International Futures (SOIF). 

The event aimed to foster future-focused collaborations across various disciplines to address challenges in digital security, identity, privacy, and trust.

The camp brought together 28 experts from academia, government, and industry for a two-day workshop to explore how technological advancements might impact democratic institutions over the next 5-15 years. 

Participants discussed potential threats and opportunities, considering actions to mitigate negative outcomes and maximize positive impacts.

Key themes and insights from the workshop will shape SPRITE+鈥檚 future initiatives, including a TIPs-focused expert meeting in May 2024 and a themed sandpit in June 2024 titled "Living in an Inauthentic World". 

This event underscores the importance of interdisciplinary collaboration in preparing for the future intersection of technology and democracy.

The report can be accessed .

On Thursday 16 May, the department of held its away day at , in central 黑料网吃瓜爆料. The event was a resounding success, fostering a collegial environment where important discussions flourished. 

Key themes included research collaborations within the department and support for early career academics. The day鈥檚 activities not only sparked meaningful conversations but also played a crucial role in shaping the future direction of the department.

During the month of April, University of 黑料网吃瓜爆料 colleagues at the UK Data Service (UKDS) delivered the following events:

• (9 April, J. Kasmire, Nadia Kennar and Louise Capener)
• (16 April, Louise Capener and Nadia Kennar)
• (17 April, J. Kasmire)
• SRMS Dissertation workshop: Locating Research Data and Data Access (18 April, Vanessa Higgins)
• (22 April, Nigel de Noronha)
• (30 April, J. Kasmire and Alle Bloom)

Slides and recordings (if available) from these events can be accessed on the event web page by clicking the links above. Recordings are also available on the .

For information about upcoming events visit the .

(Social Statistics) has been awarded an ESRC grant for the project Enhancing Data Accessibility and Security through Innovative Data Synthesis (EDASIDA).

The EDASIDA project aims to transform both data accessibility and confidentiality through innovative data synthesis techniques. In essence, the project will develop a methodology for providing tailored teaching datasets and systematic disclosure risk assessment methods.

The new methodology involves leveraging cleared analytical outputs from data services as the basis for generating synthetic data using genetic algorithms. The goal is to provide trainees with data that not only closely resembles real-world data but also yields analytical output very similar to that of the real data, enhancing the training experience. 

A pilot study conducted in collaboration with Administrative Data Research UK, demonstrated the feasibility of generating synthetic teaching datasets with both high utility and no marginal disclosure risk. The pilot dataset (a synthetic version of the linked ASHE-census dataset) was successfully used in a ADR-UK training course in April 2024.

The approach also offers a route to formalise assessment the disclosure risk associated with analytical outputs from safe settings. By embodying statistical outputs in synthetic data, it enables a systematic evaluation of disclosure risk, addressing the informality and potential inconsistencies present in current output checking procedures. 

Finally, the project aims to bolster the federated services agenda by exploring the creation of synthetic linked data from using analytical outputs from data of multiple services. This approach expands the possibilities of data synthesis without the need for actual linkage and elaborate governance of infrastructure, such as trusted third parties.

On Wednesday 24 April, the Department of hosted the grand finale of the Data Science in Practice Series. 

Karolina Michalska from KANTAR's London office captivated an audience of eager students from the MSc Social Research Methods and Statistics program and the undergraduate Data Analytics pathways.

Karolina dazzled the attendees with an exhilarating presentation showcasing her groundbreaking work at KANTAR. 

She offered an in-depth and eye-opening overview of the diverse and dynamic roles available for data scientists within the company. Her presentation was not only highly informative but also sparked a vibrant and engaging discussion, leaving everyone inspired and buzzing with ideas.

(Department of Social Statistics) has published a new article in the prestigious journal Social Networks. 

In the paper, Leifeld and his colleagues present a Bayesian framework for testing scientific expectations in Exponential Random Graph Models. 

Their framework intends to overcome some of the limitations affecting classical settings, such as inconsistent behaviour when the null hypothesis is true, their inability to quantify evidence in favour of a null hypothesis, and their inability to test multiple hypotheses with competing equality and/or order constraints on the parameters of interest in a direct manner. 

To tackle these shortcomings, this new publication presents Bayes factors and posterior probabilities for testing scientific expectations under a Bayesian framework. The methodology is implemented in the R package BFpack. The applicability of the methodology is illustrated using empirical collaboration networks and policy networks. 

Jen Murphy and , from the department of Social Statistics have just published a study in the International Journal of Population Data Science.

Using advanced modelling techniques applied to NHS administrative data, they investigated death from COVID in hospital and length of stay for surviving patients in a sample of adult patients admitted within Greater 黑料网吃瓜爆料 (N = 10,372, spell admission start dates from 30/12/2019 to 02/01/2021 inclusive). 

Their results document that deprivation was associated with death risk for hospitalised patients but not with length of stay. They further find that Male sex, co-morbidities and older age were associated with higher death risk, while male sex and co-morbidities were associated with increased length of stay. Their study also shows that black and other ethnicities stayed longer in hospital than White and Asian patients. 

Overall, the authors concluded that deprivation is important for death risk; however, the picture is complex, and the results of this analysis suggest that the reported COVID related mortality and deprivation linked reductions in life expectancy, may have occurred in the community, rather than in acute settings. 

Our colleagues , and have published a new research article in the journal Sustainable Development. The paper investigates the effect of personal income on attitudes towards climate change risk, considering the mediator of responsibility attribution (RA) for climate change and the moderator of educational attainment. 

The authors used a latent growth curve model applied to data from the UK Household Longitudinal Study dataset from 2009 to 2020 to find that personal income growth heightens the likelihood of expressing concern about climate change while reducing the propensity for holding sceptical or paradoxical attitudes over time. 

They further report that "Attributing climate change to personal behaviour mediated the income-attitude relationship. Significant differences in the mediation effect were observed between individuals who had received a university education and those who had not, even after controlling for covariates such as age, sex, political affiliation, and employment status." 

Liu, Shryane, and Elliot conclude that these findings suggest that a climate campaign emphasizing RA can address attitude disparities across income and education strata, further promoting sustainable climate action and mitigation. 

On March 22, Dr Nikita Basov received laudation as the recipient of the Friedrich Wilhelm Bessel Research Award from The Alexander von Humboldt Foundation for outstanding research achievements, as part of the Symposium for Research Award Winners in Bamberg, Germany, March 21-24, 2024. 

The award was given for major scientific contributions over the past 10 years, including the pivotal role in building the field of socio-semantic network analysis and the pioneering input into understanding the dual relationship between culture and social structure via innovative methodological combinations of statistical network modelling and interpretive analysis of ethnographic data on creative collectives. 
 

During the month of March, University of 黑料网吃瓜爆料 colleagues at the UK Data Service (UKDS) delivered the following events:

• (11 March, Alle Bloom) 
• (12 March, Jools Kasmire, Nadia Kennar and Louise Capener) 
• (13 March, Jools Kasmire and Nigel de Noronha) 
• (19 March, Jools Kasmire) 
• (27 March, Jools Kasmire and Nigel de Noronha) 

Slides and recordings (if available) from these events can be accessed on the event web page by clicking the links above. Recordings are also available on the . 
For information about upcoming events visit the . 
 

Chris Shumba, Head of Data Operations (Football) at 黑料网吃瓜爆料 United, visited the university on Wednesday, 13 March, to share insights with students enrolled in the "Data Analytics" pathways of the BASS/BAEcon programmes. 

In his talk, Shumba provided valuable perspectives on career pathways in data science and discussed the technologies shaping the field today. 

His presentation was well-received by the audience, sparking meaningful discussions and leaving attendees eager for future engagements with Shumba.

(Department of Social Statistics) has been awarded a British Academy Small Grant. The grant will fund the project titled "Statistical Modelling of Meaning Creation in Interaction: A Test of Core Social Theories.".

In this project, Dr. Basov will explore the capabilities of recently advanced network modelling methods to test two core social theories: symbolic interactionism and social constructivism. By doing so, he aims to provide the first comprehensive quantitative test of these theories.

These new methods allow the disentangling of 3-layer socio-cultural network structures that connect individuals, words, and material objects within small groups of collocated individuals. This, in turn, facilitates the cross-validation of the fundamental assumptions that both theories make about how cultural meanings are formed across different types of interactions, structural levels, and time spans.

The project will utilize a globally unique multi-dimensional dataset capturing socio-cultural dynamics in five groups of visual artists over two years.

The development of statistical models for analysing how cultural meaning is created in society will constitute a significant contribution to social science. The results of testing core social theories have the potential to catalyse fundamental changes in social sciences and beyond.

Prof. Arek Wisniowski published in February a new article in the prestigious journal 鈥淢igration Studies鈥�. This new article explains that undercounting is a critical issue in migration statistics, resulting in bias. 

It typically arises from insufficient reporting requirements and problems with enforcing such requirements. The main sources of information on undercounting are the metadata accompanying official statistics and expert opinions. 

However, metadata and arbitrary expert opinions may be limited by overlooking important details in migration data shared by various countries. This includes potential oversight of changes in methodologies, definitions, or retrospective updates to the data following censuses.

In their paper, Prof. Wisniowski and his colleagues present a methodological solution with three objectives to address undercounting in international migration data. First, they provide an overview of available metadata and expert opinions on undercounting in European migration flows. 

Second, they propose a novel data-driven approach that incorporates year-specific and duration-of-stay-adjusted classifications. The proposed methodological solution relies on comparisons of flows in the same direction reported by a given country with high-quality data reported by another set of countries. 

They use bilateral migration data provided by Eurostat, UN and selected national statistical institutes. Duration-of-stay correction coefficients are derived through an optimization model or borrowed from the literature. Metadata and expert opinion scores can also be integrated to classify undercounting. Finally, they provide a dynamic classification of undercounting for 32 European countries (2002-2019), accessible through an online Shiny application, offering flexibility and adaptability.

Their findings highlight significant undercounting in new EU member states, particularly Bulgaria, Latvia, and Romania. Interestingly, other European countries, including those presumed to maintain reliable population statistics, also exhibit notable periods of undercounting.

The UK Data Service (UKDS) ran several events in the month of February.

During the month of February, University of 黑料网吃瓜爆料 colleagues at the UK Data Service (UKDS) delivered several training courses in partnership with the Office for National Statistics, the Home Office, and the Scottish Government, among others.
 

These included:

• , 6th at UCL, London (Sarah King-Hele)
• , 7th, online (Nigel de Noronha) 
• Introduction to Machine Learning, workshop for the University of Nottingham Data Fest, 7th, online (Jools Kasmire) 
• , 13th, online (Louise Capener and Nadia Kennar)
• , 13th, online (Nadia Kennar)
• , 14th, online (Sarah King-Hele)
• , 16th, online (Nadia Kennar)

For more information about these events and future courses, you can visit the website.

Aidan O鈥橞rien and Zainab Kapasi (Data scientist at TalkTalk) came to the Department of Social Statistics to talk about their roles as Data Scientists at TalkTalk (a leading U.K. provider of mobile and internet services). 

They explained to our students how data science helps in their sector and what type of careers are available to aspiring data scientist.

A new article by (Social Statistics) has appeared in the leading international journal 鈥淧辞别迟颈肠蝉鈥�. 

The notes that in creative settings, people are often put together physically - to stimulate the exchange of ideas and practices. However, little is known of how exactly different spatial zones foster such creative sociality. 

The new paper by Basov et al. draws on a combination of interviews, observations, and surveys - analysed with an innovative mixture of abductive coding, computational space analysis, and statistical network modelling - to unveil how room sharing and object usage relate to friendships and collaborations in artistic residences. 

While social ties are indeed associated with joint material embeddedness, different types of spatial zones appear to encourage unexpected types of social ties. 

The findings inform the practice of creative space organising and the proposed approach enables explanatory analysis of the relation between material space and sociality in various contexts.
 

Are Children School Ready? Research by Dr. K. Purdam and colleagues from Social Statistics has highlighted the substantial differences in School Readiness at the individual, school and local area levels in England. 

The educational attainment levels of children in state-funded schools in England are lower than in many countries with comparable levels of economic development. There are also striking differences at the local level across England. To understand these differences it is important to examine children鈥檚 development in their early years. 

This research uses multilevel analysis of the National Pupil Database to investigate child development at ages 4 and 5 years old at the individual, school and local levels including within a case study urban area. Child development is assessed using teachers鈥� observations to measure what is termed School Readiness. This is based on a child鈥檚 communication, literacy and numeracy skills and their physical, personal and social development. 

The findings reveal substantial differences in School Readiness at the individual, school and local area levels including in terms of sex, ethnic background, age in the school year, welfare benefit entitlement and local area income deprivation level. Such differences are also evident across the separate Early Learning Goals that are used to assess School Readiness. 

Between local areas children with similar backgrounds can vary considerably in their likelihood of being categorised as School Ready. Many children face multiple disadvantages as a consequence of different interlinked factors including where they live. The gap in the levels of School Readiness has long-term implications for the individuals themselves and for society more widely. 

Whilst increasing the levels of School Readiness is a key target in the UK Government鈥檚 Levelling Up policy, tackling the stark inequalities will take considerable investment, highly targeted support and engagement across the home and school learning environments. 

The research is available to read .