Blog by the Participation Factory: “Participatory budgets (PBs) are in use in countless communities around the world, giving residents the chance to decide how to allocate parts of the public budget. They are usually open for the entire community to take part – but there can be real advantages to starting with a smaller-scale school participatory budget.
Not only do they empower pupils to get involved in local government; but they can also play a crucial role in the students’ civic education. Unlike other educational tools like mock elections, the children actually get to see how the work they put in leads to concrete results. They demonstrate the power of political engagement to children at an early age, leaving them well-placed to become active, engaged citizens in later life….
The basic setup of a school PB should allow children to get a grasp of a whole range of what we call participatory skills – including project development, public speaking, voting, running a campaign, and engaging in deliberative democratic discussions. Younger children can start out just voting for their favourite projects – but as they get older, they can begin to get more involved in the entire process, gradually building their confidence, project management skills, and their understanding of how participation works.
Participatory budgeting improves the children’s participatory skills. We have learned from our experience in Czech and Slovak schools that every year, more children feel comfortable enough to propose a project and run a campaign. They realise that there are techniques and methods to the process that they can easily learn and use, making the whole process less intimidating. They realise that debating and taking initiative doesn’t hurt, but rather leads to real results…(More)”.
CORI Blog: “Racial and ethnic diversity is one of the most commonly misunderstood aspects of rural America.
National media depictions of white farmers and ranchers in the West and Midwest, white coal miners in Appalachia, or the “white working class” living in rural communities reinforce the misconception that rural areas are homogeneously white. It is a misconception that ignores that 86 of the 100 most marginalized counties in the country are rural, 60 of which are located in Tribal lands or Southern regions with large Black populations. It is a misconception that renders invisible the 14 million Black, Hispanic or Latino, Asian, Native, and multiracial people who live in rural America (2020 census-nonmetro plus).
It is a misconception that holds significant consequences.
Misunderstandings of diversity in rural America can inhibit efforts to support programming and policies designed to increase the ability of rural communities to thrive. For rural communities to thrive, national, state, and local leaders need to take efforts to systematically address racial and ethnic inequities that limit the freedom, safety, and opportunity of rural people of color.
There is an imperative to better understand who lives in rural America today. In just the past few years, billions of public and private dollars have been committed to building a more equitable economy. The Infrastructure Investment and Jobs Act (IIJA), the CHIPS Act, and the Inflation Reduction Act (IRA) have committed hundreds of billions of dollars that will be invested by federal agencies and state and local governments in healthcare, housing, energy, and economic development.
Yet, if these historic investments are not informed by an accurate understanding of rural demographics and how these communities have evolved over time in response to government policies and settler-influenced power shifts, then we risk excluding rural communities and people of color from the critical resources that are needed to strengthen communities and economies that serve everyone.
In Part I of the second story in our Rural Aperture Project, we seek to explain how and why such flawed conceptions of rural America exist…(More)”.
Article by Justin Russell: “In the modern age, the research of Judith Glück shows that ‘wiser’ people learn valuable lessons from life’s challenges and then live happier and more fulfilling lives. On the whole, they are more connected with nature, add more to others’ lives and are less easily swayed by unreasoned rhetoric. Read Judith Glück’s Wisdom Profile on evidencebasedwisdom.com for detail on how she defines wisdom.
I have been following the research on wisdom for over a decade now, initially as part of my dissertation, In pursuit of organisational wisdom, which aimed, as part of my MSc in business psychology, to understand the relationship between wisdom and organisation leadership. Subsequently, I’ve become interested in the role that ancient wisdom has in the modern world more as a means to continually grow personally and support coaching clients.
Wisdom has only really entered into the psychological realm (as opposed to the philosophical realm) in the last few decades. Fortunately, it can draw on many previous years of research into vertical development, and generally of our understanding of other corollary ideas such as good decision-making.
While we have an incomplete picture of how wisdom develops, vertical development theories (such as those of Jane Loevinger, Erik Erikson and Robert Kegan) help us appreciate that throughout life we continue to grow and evolve, gaining new capabilities as we do. Using those capabilities is something else though, and the most developed (wisest) among us aren’t widely distributed throughout society. Through understanding wise decision-making, in Igor Grossman’s work, we know that emotional management (as measured through heart rate) is important in being able to take in all the required information and deal with it in a dispassionate (but not unfeeling) way.
As a thought experiment, I ask myself: “How would I go about making a wiser society?” The solution is highly dependent on which branch of wisdom research you attend to and so I see a threefold solution to this otherwise nebulous challenge….(More)”
Article by Max Roser: “The big visualization offers a long-term perspective on the history of technology.
The timeline begins at the center of the spiral. The first use of stone tools, 3.4 million years ago, marks the beginning of this history of technology. Each turn of the spiral then represents 200,000 years of history. It took 2.4 million years – 12 turns of the spiral – for our ancestors to control fire and use it for cooking.3
To be able to visualize the inventions in the more recent past – the last 12,000 years – I had to unroll the spiral. I needed more space to be able to show when agriculture, writing, and the wheel were invented. During this period, technological change was faster, but it was still relatively slow: several thousand years passed between each of these three inventions.
From 1800 onwards, I stretched out the timeline even further to show the many major inventions that rapidly followed one after the other.
The long-term perspective that this chart provides makes it clear just how unusually fast technological change is in our time.
You can use this visualization to see how technology developed in particular domains. Follow, for example, the history of communication: from writing, to paper, to the printing press, to the telegraph, the telephone, the radio, all the way to the Internet and smartphones.
Or follow the rapid development of human flight. In 1903, the Wright brothers took the first flight in human history (they were in the air for less than a minute), and just 66 years later, we landed on the moon. Many people saw both within their lifetimes: the first plane and the moon landing.
This large visualization also highlights the wide range of technology’s impact on our lives. It includes extraordinarily beneficial innovations, such as the vaccine that allowed humanity to eradicate smallpox, and it includes terrible innovations, like the nuclear bombs that endanger the lives of all of us.
What will the next decades bring?
The red timeline reaches up to the present and then continues in green into the future. Many children born today, even without any further increases in life expectancy, will live well into the 22nd century.
New vaccines, progress in clean, low-carbon energy, better cancer treatments – a range of future innovations could very much improve our living conditions and the environment around us. But, as I argue in a series of articles, there is one technology that could even more profoundly change our world: artificial intelligence (AI).
One reason why artificial intelligence is such an important innovation is that intelligence is the main driver of innovation itself. This fast-paced technological change could speed up even more if it’s not only driven by humanity’s intelligence, but artificial intelligence too. If this happens, the change that is currently stretched out over the course of decades might happen within very brief time spans of just a year. Possibly even faster.
I think AI technology could have a fundamentally transformative impact on our world. In many ways, it is already changing our world, as I documented in this companion article. As this technology is becoming more capable in the years and decades to come, it can give immense power to those who control it (and it poses the risk that it could escape our control entirely).
Such systems might seem hard to imagine today, but AI technology is advancing very fast. Many AI experts believe there is a real chance that human-level artificial intelligence will be developed within the next decades, as I documented in this article….(More)”.
Essay by Hannah Chafetz, Uma Kalkar, Marine Ragnet, Stefaan Verhulst: “From the regulations proposed in the European Artificial Intelligence (AI) Act to the launch of OpenAI’s ChatGPT tool, 2022 was a year that saw many policy and technological developments. Taking stock of recent data and technology trends, we offer some conjectures as to how these ideas may play out over the next year. Indeed, predictions can be dangerous, which is why we position the below as conjectures — propositions that remain tentative till more evidence emerges — that can help advance the agenda and direction of responsible use of data for the public good focus areas.
Below, we provide a summary of the five conjectures that The GovLab will track and revisit throughout 2023.
Conjecture 1. In 2023 … non-traditional data may be used with increasing frequency to solve public problems.
Complex crises, from COVID-19 to climate change, demonstrate a need for information about a variety of developments quickly and at scale. Traditional sources are not enough: growing awareness and (re)use of non-traditional data sources (NTD) to fill the gaps in traditional data cast a spotlight on the value of using and combining new data sources for problem-solving. Over the next year, NTD sources could increasingly be called upon by decision-making to address large-scale public problems.
NTD refers to data that is “digitally captured (for example, mobile phone records and financial data), mediated (for example, social media and online data), or observed (for example, satellite imagery),” using new instrumentation mechanisms and is often privately held. Our recent report discussed how COVID-19 was a “watershed moment” in terms of generating access to non-traditional health, mobility, economic, and sentiment data. As detailed in the report, decision-makers around the world increasingly recognize the potential of NTD sources when combined with traditional data responsibly. Similarly, developments in the war in Ukraine presented a pivotal moment regarding the use of NTD sources. For instance, satellite images, social media narrative trends, and real-time location mapping have supported humanitarian action and peacebuilding.
These are just two examples of the increasing interest in NTD to solve public problems. We predict that this trend could continue to expand as technological advances continue to make non-traditional data more widely available to decision-makers. Already, the financial sector is increasingly incorporating non-traditional data to inform decisions such as assessing lending risks, for example. Recently, the fintech business Nova Credit and HSBC partnered together to exploit cross-border data to allow immigrants access to credit by predicting creditworthiness via digital footprint and psychometric data. This trend is compounded by increased legislation aiming to open up the re-use of private sector data, particularly in Europe. The increased attention to NTD sources signals a need to prioritize the alignment of the supply and demand of NTD and develop a systematized approach to how it can be integrated within decision-making cycles…(More)”.
Article by Lauren Cadwallader, Lindsay Morton, and Iain Hrynaszkiewicz: “Open Science is on the rise. We can infer as much from the proliferation of Open Access publishing options; the steady upward trend in bioRxiv postings; the periodic rollout of new national, institutional, or funder policies.
But what do we actually know about the day-to-day realities of Open Science practice? What are the norms? How do they vary across different research subject areas and regions? Are Open Science practices shifting over time? Where might the next opportunity lie and where do barriers to adoption persist?
To even begin exploring these questions and others like them we need to establish a shared understanding of how we define and measure Open Science practices. We also need to understand the current state of adoption in order to track progress over time. That’s where the Open Science Indicators project comes in. PLOS conceptualized a framework for measuring Open Science practices according to the FAIR principles, and partnered with DataSeer to develop a set of numerical “indicators” linked to specific Open Science characteristics and behaviors observable in published research articles. Our very first dataset, now available for download at Figshare, focuses on three Open Science practices: data sharing, code sharing, and preprint posting…(More)”.
This is not to say that system thinkers disregard data in their work. A range of data types is used, in particular the thick, rich, qualitative data from observations, deep listening and micro-narratives. And already back in 2013, MIT researchers organized an entire conference around big data and systems thinking.
When it comes to the use of non-traditional data in the work of system innovators in international development, however, there seems to be little in terms of examples and experiences.
Enhancing system innovation?
Is there a (bigger) role to play for non-traditional data in the systems work of development organizations?
Let’s start with definitions:
A system is an interconnected set of elements that form a unified whole or serve a function.
Article by Parth Jain, Abhinay Mannepalli, Raj Parikh, and Jim Samuel: “Optical character recognition (OCR) is growing at a projected compounded annual growth rate (CAGR) of 16%, and is expected to have a value of 39.7 billion USD by 2030, as estimated by Straits research. There has been a growing interest in OCR technologies over the past decade. Optical character recognition is the technological process for transforming images of typed, handwritten, scanned, or printed texts into machine-encoded and machine-readable texts (Tappert, et al., 1990). OCR can be used with a broad range of image or scan formats – for example, these could be in the form of a scanned document such as a .pdf file, a picture of a piece of paper in .png or .jpeg format, or images with embedded text, such as characters on a coffee cup, title on the cover page of a book, the license number on vehicular plates, and images of code on websites. OCR has proven to be a valuable technological process for tackling the important challenge of transforming non-machine-readable data into machine readable data. This enables the use of natural language processing and computational methods on information-rich data which were previously largely non-processable. Given the broad array of scanned and image documents in open government data and other open data sources, OCR holds tremendous promise for value generation with open data.
Open data has been defined as “being data that is made freely available for open consumption, at no direct cost to the public, which can be efficiently located, filtered, downloaded, processed, shared, and reused without any significant restrictions on associated derivatives, use, and reuse” (Chidipothu et al., 2022). Large segments of open data contain images, visuals, scans, and other non-machine-readable content. The size and complexity associated with the manual analysis of such content is prohibitive. The most efficient way would be to establish standardized processes for transforming documents into their OCR output versions. Such machine-readable text could then be analyzed using a range of NLP methods. Artificial Intelligence (AI) can be viewed as being a “set of technologies that mimic the functions and expressions of human intelligence, specifically cognition and logic” (Samuel, 2021). OCR was one of the earliest AI technologies implemented. The first ever optical reader to identify handwritten numerals was the advanced reading machine “IBM 1287,” presented at the World Fair in New York in 1965 (Mori, et al., 1990). The value of open data is well established – however, the extent of usefulness of open data is dependent on “accessibility, machine readability, quality” and the degree to which data can be processed by using analytical and NLP methods (data.gov, 2022; John, et al., 2022)…(More)”
Blog by Adam Mastroianni: “For the last 60 years or so, science has been running an experiment on itself. The experimental design wasn’t great; there was no randomization and no control group. Nobody was in charge, exactly, and nobody was really taking consistent measurements. And yet it was the most massive experiment ever run, and it included every scientist on Earth.
Most of those folks didn’t even realize they were in an experiment. Many of them, including me, weren’t born when the experiment started. If we had noticed what was going on, maybe we would have demanded a basic level of scientific rigor. Maybe nobody objected because the hypothesis seemed so obviously true: science will be better off if we have someone check every paper and reject the ones that don’t pass muster. They called it “peer review.”
This was a massive change. From antiquity to modernity, scientists wrote letters and circulated monographs, and the main barriers stopping them from communicating their findings were the cost of paper, postage, or a printing press, or on rare occasions, the cost of a visit from the Catholic Church. Scientific journals appeared in the 1600s, but they operated more like magazines or newsletters, and their processes of picking articles ranged from “we print whatever we get” to “the editor asks his friend what he thinks” to “the whole society votes.” Sometimes journals couldn’t get enough papers to publish, so editors had to go around begging their friends to submit manuscripts, or fill the space themselves. Scientific publishing remained a hodgepodge for centuries.
(Only one of Einstein’s papers was ever peer-reviewed, by the way, and he was so surprised and upset that he published his paper in a different journal instead.)
That all changed after World War II. Governments poured funding into research, and they convened “peer reviewers” to ensure they weren’t wasting their money on foolish proposals. That funding turned into a deluge of papers, and journals that previously struggled to fill their pages now struggled to pick which articles to print. Reviewing papers before publication, which was “quite rare” until the 1960s, became much more common. Then it became universal.
Now pretty much every journal uses outside experts to vet papers, and papers that don’t please reviewers get rejected. You can still write to your friends about your findings, but hiring committees and grant agencies act as if the only science that exists is the stuff published in peer-reviewed journals. This is the grand experiment we’ve been running for six decades.
Blog by Anna Ibru and Dane Gambrell at The GovLab: “…In recent years, public institutions around the world are piloting new youth engagement initiatives like Creamos that tap the expertise and experiences of young people to develop projects, programs, and policies and address complex social challenges within communities.
To learn from and scale best practices from international models of youth engagement, The GovLab has develop case studies about three path breaking initiatives: Nuortenbudjetti, Helsinki’s participatory budgeting initiative for youth; Forum Jove BCN, Barcelona’s youth led citizens’ assembly; and Creamos, an open innovation and coaching program for young social innovators in Chile. For government decision makers and institutions who are looking to engage and empower young people to get involved in their communities, develop real-world solutions, and strengthen democracy, these examples describe these initiatives and their outcomes along with guidance on how to design and replicate such projects in your community. Young people are still a widely untapped resource who are too-often left out in policy and program design. The United Nations affirms that it is impossible to meet the UN SDGs by 2030 without active participation of the 1.8 billion youth in the world. Government decision makers and institutions must capitalize on the opportunity to engage and empower young people. The successes of Nuortenbudjetti, Forum Jove BCN, and Creamos provide a roadmap for policymakers looking to engage in this space….(More)” See also: Nuortenbudjetti: Helsinki’s Youth Budget; Creamos: Co-creating youth-led social innovation projects in Chile and Forum Jove BCN: Barcelona’s Youth Forum.
