Open Access Book edited by Gorgi Krlev, Dominika Wruk, Giulio Pasi, and Marika Bernhard: “Lack of progress in the area of global sustainable development and difficulties in crisis management highlight the need to transform the economy and find new ways of making society more resilient. The social economy is increasingly recognized as a driver of such transformations; it comprises traditional forms of cooperative or solidarity-based organizations alongside new phenomena such as impact investing or social tech ventures that aim to contribute to the public good. Social Economy Science provides the first comprehensive analysis of why and how social economy organizations create superior value for society. The book draws on organizational theory and transition studies to provide a systematic perspective on complex multi-stakeholder forms of action. It discusses the social economy’s role in promoting innovation for impact, as well as its role as an agent of societal change and as a partner to businesses, governments, and citizens…(More)”.
The public good of statistics – narratives from around the world
Blog by Ken Roy:” I have been looking at some of the narratives used by bodies producing Official Statistics – specifically those in a sample of recent strategies and business plans from different National Statistical Offices. Inevitably these documents focus on planned programmes of work – the key statistical outputs, the technical and methodological investments etc – and occasionally on interesting things like budgets.
When these documents touch on the rationale for (or purpose of) Official Statistics, one approach is to present Official Statistics as a ‘right’ of citizens or as essential national infrastructure. For example Statistics Finland frame Official Statistics as “our shared national capital”. A further common approach is to reference the broad purpose of improved decision making – Statistics Canada has the aim that “Canadians have the key information they need to make evidence-based decisions.”
Looking beyond these high-level statements, I was keen to find any further, more specific, expressions of real-world impacts. The following sets out some initial groups of ideas and some representative quotes.
In terms of direct impacts for citizens, some strategies have a headline aim that citizens are knowledgeable about their world – Statistics Iceland aims to enable an “informed society”. A slightly different ambition is that different groups of citizens are represented or ‘seen’ by Official Statistics. The UK Statistics Authority aims to “reflect the experiences of everyone in our society so that everyone counts, and is counted, and no one is forgotten”. There are also references to the role of Official Statistics (and data more broadly) in empowering citizens – most commonly through giving them the means to hold government to account. One of the headline aims of New Zealand’s Data Investment Plan is that “government is held to account through a robust and transparent data system”.
Also relevant to citizens is the ambition for Official Statistics to enable healthy, informed public debate – one aim of the Australian Bureau of Statistics is that their work will “provide reliable information on a range of matters critical to public debate”.
Some narratives hint at the contribution of Official Statistics systems to national economic success. Stats NZ notes that “the integrity of official data can have wide-ranging implications … such as the interest charged on government borrowing.” The Papua New Guinea statistics office references a focus on “private sector investors who want to use data and statistics to aid investment decisions”.
Finally, we come to governments. Official Statistics are regularly presented as essential to a better, more effective, government process – through establishing understanding of the circumstances and needs of citizens, businesses and places and hence supporting the development and implementation of better policies, programmes and services in response. The National Bureau of Statistics (Tanzania) sees Official Statistics as enabling “evidence-based formulation, planning, monitoring and evaluation which are key in the realization of development aspirations.” A related theme is the contribution to good governance – the United Nations presents Official Statistics as “an essential element of the accountability of governments and public bodies to the public in a democratic society…(More)”.
The Time is Now: Establishing a Mutual Commitment Framework (MCF) to Accelerate Data Collaboratives
Article by Stefaan Verhulst, Andrew Schroeder and William Hoffman: “The key to unlocking the value of data lies in responsibly lowering the barriers and shared risks of data access, re-use, and collaboration in the public interest. Data collaboratives, which foster responsible access and re-use of data among diverse stakeholders, provide a solution to these challenges.
Today, however, setting up data collaboratives takes too much time and is prone to multiple delays, hindering our ability to understand and respond swiftly and effectively to urgent global crises. The readiness of data collaboratives during crises faces key obstacles in terms of data use agreements, technical infrastructure, vetted and reproducible methodologies, and a clear understanding of the questions which may be answered more effectively with additional data.
Organizations aiming to create data collaboratives often face additional challenges, as they often lack established operational protocols and practices which can streamline implementation, reduce costs, and save time. New regulations are emerging that should help drive the adoption of standard protocols and processes. In particular, the EU Data Governance Act and the forthcoming Data Act aim to enable responsible data collaboration. Concepts like data spaces and rulebooks seek to build trust and strike a balance between regulation and technological innovation.
This working paper advances the case for creating a Mutual Commitment Framework (MCF) in advance of a crisis that can serve as a necessary and practical means to break through chronic choke points and shorten response times. By accelerating the establishment of operational (and legally cognizable) data collaboratives, duties of care can be defined and a stronger sense of trust, clarity, and purpose can be instilled among participating entities. This structured approach ensures that data sharing and processing are conducted within well-defined, pre-authorized boundaries, thereby lowering shared risks and promoting a conducive environment for collaboration…(More)”.
Open Government for Stronger Democracies
A Global Assessment by the OECD: “Open government is a powerful catalyst for driving democracy, public trust, and inclusive growth. In recognition of this, the OECD Council adopted the Recommendation on Open Government in 2017. To date, it remains the first – and only – internationally recognised legal instrument on open government and has guided many countries in designing and implementing their open government agendas. This report takes stock of countries’ implementation of the Recommendation, its dissemination, and its ongoing significance. It is based on an OECD survey carried out in 2020/2021 among all countries that adhered to the Recommendation and other partner countries, as well as on further data collected through a perception survey with delegates to the OECD Working Party on Open Government…(More)”.
Innovation in Anticipation for Migration: A Deep Dive into Methods, Tools, and Data Sources
Blog by Sara Marcucci and Stefaan Verhulst: “In the ever-evolving landscape of anticipatory methods for migration policy, innovation is a dynamic force propelling the field forward. This seems to be happening in two main ways: first, as we mentioned in our previous blog, one of the significant shifts lies in the blurring of boundaries between quantitative forecasting and qualitative foresight, as emerging mixed-method approaches challenge traditional paradigms. This transformation opens up new pathways for understanding complex phenomena, particularly in the context of human migration flows.
Second, the innovation happening today is not necessarily rooted in the development of entirely new methodologies, but rather in how existing methods are adapted and enhanced. Indeed, innovation seems to extend to the utilization of diverse tools and data sources that bolster the effectiveness of existing methods, offering a more comprehensive and timely perspective on migration trends.
In the context of this blog series, methods refer to the various approaches and techniques used to anticipate and analyze migration trends, challenges, and opportunities. These methods are employed to make informed decisions and develop policies related to human migration. They can include a wide range of strategies to gather and interpret data and insights in the field of migration policy.
Tools, on the other hand, refer to the specific instruments or technologies used to support and enhance the effectiveness of these methods. They encompass a diverse set of resources and technologies that facilitate data collection, analysis, and decision-making in the context of migration policy. These tools can include both quantitative and qualitative data collection and analysis tools, as well as innovative data sources, software, and techniques that help enhance anticipatory methods.
This blog aims to deep dive into the main anticipatory methods adopted in the field of migration, as well as some of the tools and data sources employed to enhance and experiment with them. First, the blog will provide a list of methods considered; second, it will illustrate the main innovative tools employed, and finally it will provide a set of new, non-traditional data sources that are increasingly being used to feed anticipatory methods…(More)”.
Hopes over fears: Can democratic deliberation increase positive emotions concerning the future?
Paper by Mikko Leino and Katariina Kulha: “Deliberative mini-publics have often been considered to be a potential way to promote future-oriented thinking. Still, thinking about the future can be hard as it can evoke negative emotions such as stress and anxiety. This article establishes why a more positive outlook towards the future can benefit long-term decision-making. Then, it explores whether and to what extent deliberative mini-publics can facilitate thinking about the future by moderating negative emotions and encouraging positive emotions. We analyzed an online mini-public held in the region of Satakunta, Finland, organized to involve the public in the drafting process of a regional plan extending until the year 2050. In addition to the standard practices related to mini-publics, the Citizens’ Assembly included an imaginary time travel exercise, Future Design, carried out with half of the participants. Our analysis makes use of both survey and qualitative data. We found that democratic deliberation can promote positive emotions, like hopefulness and compassion, and lessen negative emotions, such as fear and confusion, related to the future. There were, however, differences in how emotions developed in the various small groups. Interviews with participants shed further light onto how participants felt during the event and how their sentiments concerning the future changed…(More)”.
Why the future might not be where you think it is
Article by Ruth Ogden: “Imagine the future. Where is it for you? Do you see yourself striding towards it? Perhaps it’s behind you. Maybe it’s even above you.
And what about the past? Do you imagine looking over your shoulder to see it?
How you answer these questions will depend on who you are and where you come from. The way we picture the future is influenced by the culture we grow up in and the languages we are exposed to.
For many people who grew up in the UK, the US and much of Europe, the future is in front of them, and the past is behind them. People in these cultures typically perceive time as linear. They see themselves as continually moving towards the future because they cannot go back to the past.
In some other cultures, however, the location of the past and the future are inverted. The Aymara, a South American Indigenous group of people living in the Andes, conceptualise the future as behind them and the past in front of them.
Scientists discovered this by studying the gestures of the Aymara people during discussions of topics such as ancestors and traditions. The researchers noticed that when Aymara spoke about their ancestors, they were likely to gesture in front of themselves, indicating that the past was in front. However, when they were asked about a future event, their gesture seemed to indicate that the future was perceived as behind.
Analysis of how people write, speak and gesture about time suggests that the Aymara are not alone. Speakers of Darij, an Arabic dialect spoken in Morocco, also appear to imagine the past as in front and the future behind. As do some Vietnamese speakers.
The future doesn’t always have to be behind or in front of us. There is evidence that some Mandarin speakers represent the future as down and the past as up. These differences suggest that there is no universal location for the past, present and future. Instead, people construct these representations based on their upbringing and surroundings.
Culture doesn’t just influence where we see the position of the future. It also influences how we see ourselves getting there…(More)”.
The State of Open Data 2023
Report by Springer Nature, Digital Science and Figshare: “The 2023 survey showed that the key motivations for researchers to share their data remain very similar to previous years, with full citation of research papers or a data citation ranking highly. 89% of respondents also said they make their data available publicly, however almost three quarters of respondents had never received support with planning, managing or sharing research data.
One size does not fit all: Variations in responses from different areas of expertise and geographies highlight a need for a more nuanced approach to research data management support globally. For example, 64% of respondents supported the idea of a national mandate for making research data openly available, with Indian and German respondents more likely to support this idea (both 71%).
Credit is an ongoing issue: For eight years running, our survey has revealed a recurring concern among researchers: the perception that they don’t receive sufficient recognition for openly sharing their data. 60% of respondents said they receive too little credit for sharing their data.
AI awareness hasn’t translated to action: For the first time, this year we asked survey respondents to indicate if they were using ChatGPT or similar AI tools for data collection, data processing and metadata collection. The most common response to all three questions was ‘I’m aware of these tools but haven’t considered it.’..(More)”.
Data Governance and Privacy Challenges in the Digital Healthcare Revolution
Paper by Nargiz Kazimova: “The onset of the COVID-19 pandemic has catalyzed an imperative for digital transformation in the healthcare sector. This study investigates the accelerated shift towards a digitally-enhanced healthcare delivery system, advocating for the widespread adoption of telemedicine and the relaxation of regulatory barriers. The paper also scrutinizes the burgeoning use of electronic health records, wearable devices, artificial intelligence, and machine learning, and how these technologies offer promising avenues for improving patient care and medical outcomes. Despite the advancements, the rapid digital integration raises significant privacy and security concerns. The stigma associated with certain illnesses and potential discrimination presents serious challenges that digital healthcare innovations can exacerbate.
This research underscores the criticality of stringent data governance to safeguard personal health information in the face of growing digitalization. The analysis begins with an exploration of the data governance role in optimizing healthcare outcomes and preserving privacy, followed by an assessment of the breadth and depth of health data proliferation. The paper subsequently navigates the complex legal and ethical terrain, contrasting HIPAA and GDPR frameworks to underline the current regulatory challenges.
A comprehensive set of strategic recommendations is provided for reinforcing data governance and enhancing privacy protection in healthcare. The author advises on updating legal provisions to match the dynamic healthcare environment, widening the scope of privacy laws, and improving the transparency of data-sharing practices. The establishment of ethical guidelines for the collection and use of health data is also recommended, focusing on explicit consent, decision-making transparency, harm accountability, maintenance of data anonymity, and the mitigation of biases in datasets.
Moreover, the study advocates for stronger transparency in data sharing with clear communication on data use, rigorous internal and external audit mechanisms, and informed consent processes. The conclusion calls for increased collaboration between healthcare providers, patients, administrative staff, ethicists, regulators, and technology companies to create governance models that reconcile patient rights with the expansive use of health data. The paper culminates in a call to action for a balanced approach to privacy and innovation in the data-driven era of healthcare…(More)”.
The AI regulations that aren’t being talked about
Article by Deloitte: “…But our research shows that this focus may be overlooking some of the most important tools already on the books. Of the 1,600+ policies we analyzed, only 11% were focused on regulating AI-adjacent issues like data privacy, cybersecurity, intellectual property, and so on (Figure 5). Even when limiting the search to only regulations, 60% were focused directly on AI and only 40% on AI-adjacent issues (Figure 5). For example, several countries have data protection agencies with regulatory powers to help protect citizens’ data privacy. But while these agencies may not have AI or machine learning named specifically in their charters, the importance of data in training and using AI models makes them an important AI-adjacent tool.
This can be problematic because directly regulating a fast-moving technology like AI can be difficult. Take the hypothetical example of removing bias from home loan decisions. Regulators could accomplish this goal by mandating that AI should have certain types of training data to ensure that the models are representative and will not produce biased results, but such an approach can become outdated when new methods of training AI models emerge. Given the diversity of different types of AI models already in use, from recurrent neural networks to generative pretrained transformers to generative adversarial networks and more, finding a single set of rules that can deliver what the public desires both now, and in the future, may be a challenge…(More)”.