The Socio-Legal Lab: An Experiential Approach to Research on Law in Action

Curated on Posted on by Stefaan Verhulst

Guide by Siddharth Peter de Souza and Lisa Hahn: “..interactive workbook for socio-legal research projects. It employs the idea of a “lab” as a space for interactive and experiential learning. As an introductory book, it addresses researchers of all levels who are beginning to explore interdisciplinary research on law and are looking for guidance on how to do so. Likewise, the book can be used by teachers and peer groups to experiment with teaching and thinking about law in action through lab-based learning…

The book covers themes and questions that may arise during a socio-legal research project. This starts with examining what research and interdisciplinarity mean and in which forms they can be practiced. After an overview of the research process, we will discuss how research in action is often unpredictable and messy. Thus, the practical and ethical challenges of doing research will be discussed along with processes of knowledge production and assumptions that we have as researchers. 

Conducting a socio-legal research project further requires an overview of the theoretical landscape. We will introduce general debates about the nature, functions, and effects of law in society. Further, common dichotomies in socio-legal research such as “law” and “the social” or “qualitative” and “quantitative” research will be explored, along with suggested ways on how to bridge them. 

Turning to the application side of socio-legal research, the book delves deeper into questions of data on law and society, where to collect it and how to deal with it in a reflexive manner. It discusses different methods of qualitative socio-legal research and offers ways in which they can be experienced through exercises and simulations. In the research process, generating research results is followed by publishing and communicating them. We will explore different ways to ensure the outreach and impact of one’s research by communicating results through journals, blogs or social media. Finally, the book also discusses academia as a social space and the value of creating and using networks and peer groups for mutual support.

Overall, the workbook is designed to accompany and inspire researchers on their way through a socio-legal research project and to empower the reader into thinking more creatively about their methods, while at the same time demystifying them…(More)”.

Data Analysis for Social Science: A Friendly and Practical Introduction

Curated on Posted on by Stefaan Verhulst

Book by Elena Llaudet and Kosuke Imai: “…provides a friendly introduction to the statistical concepts and programming skills needed to conduct and evaluate social scientific studies. Using plain language and assuming no prior knowledge of statistics and coding, the book provides a step-by-step guide to analyzing real-world data with the statistical program R for the purpose of answering a wide range of substantive social science questions. It teaches not only how to perform the analyses but also how to interpret results and identify strengths and limitations. This one-of-a-kind textbook includes supplemental materials to accommodate students with minimal knowledge of math and clearly identifies sections with more advanced material so that readers can skip them if they so choose…(More)”.

We could run out of data to train AI language programs 

Curated on Posted on by Stefaan Verhulst

Article by Tammy Xu: “Large language models are one of the hottest areas of AI research right now, with companies racing to release programs like GPT-3 that can write impressively coherent articles and even computer code. But there’s a problem looming on the horizon, according to a team of AI forecasters: we might run out of data to train them on.

Language models are trained using texts from sources like Wikipedia, news articles, scientific papers, and books. In recent years, the trend has been to train these models on more and more data in the hope that it’ll make them more accurate and versatile.

The trouble is, the types of data typically used for training language models may be used up in the near future—as early as 2026, according to a paper by researchers from Epoch, an AI research and forecasting organization, that is yet to be peer reviewed. The issue stems from the fact that, as researchers build more powerful models with greater capabilities, they have to find ever more texts to train them on. Large language model researchers are increasingly concerned that they are going to run out of this sort of data, says Teven Le Scao, a researcher at AI company Hugging Face, who was not involved in Epoch’s work.

The issue stems partly from the fact that language AI researchers filter the data they use to train models into two categories: high quality and low quality. The line between the two categories can be fuzzy, says Pablo Villalobos, a staff researcher at Epoch and the lead author of the paper, but text from the former is viewed as better-written and is often produced by professional writers…(More)”.

How many yottabytes in a quettabyte? Extreme numbers get new names

Curated on Posted on by Stefaan Verhulst

Article by Elizabeth Gibney: “By the 2030s, the world will generate around a yottabyte of data per year — that’s 1024 bytes, or the amount that would fit on DVDs stacked all the way to Mars. Now, the booming growth of the data sphere has prompted the governors of the metric system to agree on new prefixes beyond that magnitude, to describe the outrageously big and small.

Representatives from governments worldwide, meeting at the General Conference on Weights and Measures (CGPM) outside Paris on 18 November, voted to introduce four new prefixes to the International System of Units (SI) with immediate effect. The prefixes ronna and quetta represent 1027 and 1030, and ronto and quecto signify 10−27 and 10−30. Earth weighs around one ronnagram, and an electron’s mass is about one quectogram.

This is the first update to the prefix system since 1991, when the organization added zetta (1021), zepto (10−21), yotta (1024) and yocto (10−24). In that case, metrologists were adapting to fit the needs of chemists, who wanted a way to express SI units on the scale of Avogadro’s number — the 6 × 1023 units in a mole, a measure of the quantity of substances. The more familiar prefixes peta and exa were added in 1975 (see ‘Extreme figures’).

Extreme figures

Advances in scientific fields have led to increasing need for prefixes to describe very large and very small numbers.

FactorNameSymbolAdopted
1030quettaQ2022
1027ronnaR2022
1024yottaY1991
1021zettaZ1991
1018exaE1975
1015petaP1975
10−15femtof1964
10−18attoa1964
10−21zeptoz1991
10−24yoctoy1991
10−27rontor2022
10−30quectoq2022

Prefixes are agreed at the General Conference on Weights and Measures.

Today, the driver is data science, says Richard Brown, a metrologist at the UK National Physical Laboratory in Teddington. He has been working on plans to introduce the latest prefixes for five years, and presented the proposal to the CGPM on 17 November. With the annual volume of data generated globally having already hit zettabytes, informal suggestions for 1027 — including ‘hella’ and ‘bronto’ — were starting to take hold, he says. Google’s unit converter, for example, already tells users that 1,000 yottabytes is 1 hellabyte, and at least one UK government website quotes brontobyte as the correct term….(More)”

Is Facebook’s advertising data accurate enough for use in social science research? Insights from a cross-national online survey

Curated on Posted on by Stefaan Verhulst

Paper by André Grow et al: “Social scientists increasingly use Facebook’s advertising platform for research, either in the form of conducting digital censuses of the general population, or for recruiting participants for survey research. Both approaches depend on the accuracy of the data that Facebook provides about its users, but little is known about how accurate these data are. We address this gap in a large-scale, cross-national online survey (N = 137,224), in which we compare self-reported and Facebook-classified demographic information (sex, age and region of residence). Our results suggest that Facebook’s advertising platform can be fruitfully used for conducing social science research if additional steps are taken to assess the accuracy of the characteristics under consideration…(More)”.

Humanizing Science and Engineering for the Twenty-First Century

Curated on Posted on by Stefaan Verhulst

Essay by Kaye Husbands Fealing, Aubrey Deveny Incorvaia and Richard Utz: “Solving complex problems is never a purely technical or scientific matter. When science or technology advances, insights and innovations must be carefully communicated to policymakers and the public. Moreover, scientists, engineers, and technologists must draw on subject matter expertise in other domains to understand the full magnitude of the problems they seek to solve. And interdisciplinary awareness is essential to ensure that taxpayer-funded policy and research are efficient and equitable and are accountable to citizens at large—including members of traditionally marginalized communities…(More)”.

Science and the World Cup: how big data is transforming football

Curated on Posted on by Stefaan Verhulst

Essay by David Adam: “The scowl on Cristiano Ronaldo’s face made international headlines last month when the Portuguese superstar was pulled from a match between Manchester United and Newcastle with 18 minutes left to play. But he’s not alone in his sentiment. Few footballers agree with a manager’s decision to substitute them in favour of a fresh replacement.

During the upcoming football World Cup tournament in Qatar, players will have a more evidence-based way to argue for time on the pitch. Within minutes of the final whistle, tournament organizers will send each player a detailed breakdown of their performance. Strikers will be able to show how often they made a run and were ignored. Defenders will have data on how much they hassled and harried the opposing team when it had possession.

It’s the latest incursion of numbers into the beautiful game. Data analysis now helps to steer everything from player transfers and the intensity of training, to targeting opponents and recommending the best direction to kick the ball at any point on the pitch.

Meanwhile, footballers face the kind of data scrutiny more often associated with an astronaut. Wearable vests and straps can now sense motion, track position with GPS and count the number of shots taken with each foot. Cameras at multiple angles capture everything from headers won to how long players keep the ball. And to make sense of this information, most elite football teams now employ data analysts, including mathematicians, data scientists and physicists plucked from top companies and labs such as computing giant Microsoft and CERN, Europe’s particle-physics laboratory near Geneva, Switzerland….(More)”.

The network science of collective intelligence

Curated on Posted on by Stefaan Verhulst

Article by Damon Centola: “In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively. I conclude by highlighting current theoretical problems at the forefront of research on networked collective intelligence, as well as vital public policy challenges that require new research efforts…(More)”.

Meaningful public engagement in the context of open science: reflections from early and mid-career academics

Curated on Posted on by Stefaan Verhulst

Paper by Wouter Boon et al: “How is public engagement perceived to contribute to open science? This commentary highlights common reflections on this question from interviews with 12 public engagement fellows in Utrecht University’s Open Science Programme in the Netherlands. We identify four reasons why public engagement is an essential enabler of open science. Interaction between academics and society can: (1) better align science with the needs of society; (2) secure a relationship of trust between science and society; (3) increase the quality and impact of science; and (4) support the impact of open access and FAIR data practices (data which meet principles of findability, accessibility, interoperability and reusability). To be successful and sustainable, such public engagement requires support in skills training and a form of institutionalisation in a university-wide system, but, most of all, the fellows express the importance of a formal and informal recognition and rewards system. Our findings suggest that in order to make public engagement an integral part of open science, universities should invest in institutional support, create awareness, and stimulate dialogue among staff members on how to ‘do’ good public engagement….(More)”.

Data Structures the Fun Way

Curated on Posted on by Stefaan Verhulst

Book by Jeremy Kubica: “This accessible and entertaining book provides an in-depth introduction to computational thinking through the lens of data structures — a critical component in any programming endeavor. Through diagrams, pseudocode, and humorous analogies, you’ll learn how the structure of data drives algorithmic operations, gaining insight into not just how to build data structures, but precisely how and when to use them. 

This book will give you a strong background in implementing and working with more than 15 key data structures, from stacks, queues, and caches to bloom filters, skip lists, and graphs. Master linked lists by standing in line at a cafe, hash tables by cataloging the history of the summer Olympics, and Quadtrees by neatly organizing your kitchen cabinets. Along with basic computer science concepts like recursion and iteration, you’ll learn: 

  • The complexity and power of pointers
  • The branching logic of tree-based data structures
  • How different data structures insert and delete data in memory
  • Why mathematical mappings and randomization are useful
  • How to make tradeoffs between speed, flexibility, and memory usage

Data Structures the Fun Way shows how to efficiently apply these ideas to real-world problems—a surprising number of which focus on procuring a decent cup of coffee. At any level, fully understanding data structures will teach you core skills that apply across multiple programming languages, taking your career to the next level….(More)”.