science

Using data and design to support people to stay in work

Curated on January 30, 2017August 3, 2018 by Stefaan Verhulst

Cat Drew at Civil Service Quarterly: “…Data and digital are fairly understandable concepts in policy-making. But design? Why is it one of the three Ds?

Policy Lab believes that design approaches are particularly suited to complex issues that have multiple causes and for which there is no one, simple answer. Design encourages people to think about the user’s needs (not just the organisation’s needs), brings in different perspectives to innovate new ideas, and then prototypes (mocks them up and tries them out) to iteratively improve ideas until they find one that can be scaled up.

Composite graph and segmentation analysis collection — Segmentation analysis of those who reported being on health-related benefits in the Understanding Society survey

Policy Lab also recognises that data alone cannot solve policy problems, and has been experimenting with how to combine numerical and more human practices. Data can explain what is happening, while design research methods – such as ethnography, observing people’s behaviours – can explain why things are happening. Data can be used to automate and tailor public services; while design means frontline delivery staff and citizens will actually know about and use them. Data-rich evidence is highly valued by policy-makers; and design can make it understandable and accessible to a wider group of people, opening up policy-making in the process.

The Lab is also experimenting with new data methods.

Data science can be used to look at complex, unstructured data (social media data, for example), in real time. Digital data, such as social media data or internet searches, can reveal how people behave (rather than how they say they behave). It can also look at huge amounts of data far quicker than humans, and find unexpected patterns hidden in the data. Powerful computers can identify trends from historical data and use these to predict what might happen in the future.

Supporting people in work project

The project took a DDD approach to generating insight and then creating ideas. The team (including the data science organisation Mastodon C and design agency Uscreates) used data science techniques together with ethnography to create a rich picture about what was happening. Then it used design methods to create ideas for digital services with the user in mind, and these were prototyped and tested with users.

The data science confirmed many of the known risk factors, but also revealed some new insights. It told us what was happening at scale, and the ethnography explained why.

The data science showed that people were more likely to go onto sickness benefits if they had been in the job a shorter time. The ethnography explained that the relationship with the line manager and a sense of loyalty were key factors in whether someone stayed in work or went onto benefits.

The data science showed that women with clinical depression were less likely to go onto sickness benefits than men with the same condition. The ethnography revealed how this played out in real life:

For example, Ella [not her real name], a teacher from London who had been battling with depression at work for a long time but felt unable to go to her boss about it. She said she was “relieved” when she got cancer, because she could talk to her boss about a physical condition and got time off to deal with both illnesses.

The data science also allowed the segmentation of groups of people who said they were on health-related benefits. Firstly, the clustering revealed that two groups had average health ratings, indicating that other non-health-related issues might be driving this. Secondly, it showed that these two groups were very different (one older group of men with previously high pay and working hours; the other of much younger men with previously low pay and working hours). The conclusion was that their motivations and needs to stay in work – and policy interventions – would be different.

The ethnography highlighted other issues that were not captured in the data but would be important in designing solutions, such as: a lack of shared information across the system; the need of the general practitioner (GP) to refer patients to other non-health services as well as providing a fit note; and the importance of coaching, confidence-building and planning….(More)”

GSK and MIT Flumoji app tracks influenza outbreaks with crowdsourcing

Curated on January 29, 2017August 3, 2018 by Stefaan Verhulst

Beth Snyder Bulik at FiercePharma: “It’s like Waze for the flu. A new GlaxoSmithKline-sponsored app called Flumoji uses crowdsourced data to track influenza movement in real time.

Developed with MIT’s Connection Science, the Flumoji app gathers data passively and identifies fluctuations in users’ activity and social interactions to try to identify when a person gets the flu. The activity data is combined with traditional flu tracking data from the Centers for Disease Control to help determine outbreaks. The Flumoji study runs through April, when it will be taken down from the Android app store and no more data will be collected from users.

To make the app more engaging for users, Flumoji uses emojis to help users identify how they’re feeling. If it’s a flu day, symptom faces with thermometers, runny noses and coughs can be chosen, while on other days, users can show how they’re feeling with more traditional mood emojis.

The app has been installed on 500-1,000 Android phones, according to Google Play data.

“Mobile phones are a widely available and efficient way to monitor patient health. GSK has been using them in its studies to monitor activity and vital signs in study patients, and collect patient feedback to improve decision making in the development of new medicines. Tracking the flu is just the latest test of this technology,” Mary Anne Rhyne, a GSK director of external communications for R&D in the U.S., told FiercePharma in an email interview…(More)”

Quantifying scenic areas using crowdsourced data

Curated on January 29, 2017August 3, 2018 by Stefaan Verhulst

Chanuki Illushka Seresinhe, Helen Susannah Moat and Tobias Preis in Environment and Planning B: Urban Analytics and City Science: “For centuries, philosophers, policy-makers and urban planners have debated whether aesthetically pleasing surroundings can improve our wellbeing. To date, quantifying how scenic an area is has proved challenging, due to the difficulty of gathering large-scale measurements of scenicness. In this study we ask whether images uploaded to the website Flickr, combined with crowdsourced geographic data from OpenStreetMap, can help us estimate how scenic people consider an area to be. We validate our findings using crowdsourced data from Scenic-Or-Not, a website where users rate the scenicness of photos from all around Great Britain. We find that models including crowdsourced data from Flickr and OpenStreetMap can generate more accurate estimates of scenicness than models that consider only basic census measurements such as population density or whether an area is urban or rural. Our results provide evidence that by exploiting the vast quantity of data generated on the Internet, scientists and policy-makers may be able to develop a better understanding of people’s subjective experience of the environment in which they live….(More)”

The science of society: From credible social science to better social policies

Curated on January 24, 2017August 3, 2018 by Stefaan Verhulst

Nancy Cartwright and Julian Reiss at LSE Blog: “Society invests a great deal of money in social science research. Surely the expectation is that some of it will be useful not only for understanding ourselves and the societies we live in but also for changing them? This is certainly the hope of the very active evidence-based policy and practice movement, which is heavily endorsed in the UK both by the last Labour Government and by the current Coalition Government. But we still do not know how to use the results of social science in order to improve society. This has to change, and soon.

Last year the UK launched an extensive – and expensive – new What Works Network that, as the Government press release describes, consists of “two existing centres of excellence – the National Institute for Health and Clinical Excellence (NICE) and the Educational Endowment Foundation – plus four new independent institutions responsible for gathering, assessing and sharing the most robust evidence to inform policy and service delivery in tackling crime, promoting active and independent ageing, effective early intervention, and fostering local economic growth”.

This is an exciting and promising initiative. But it faces a series challenge: we remain unable to build real social policies based on the results of social science or to predict reliably what the outcomes of these policies will actually be. This contrasts with our understanding of how to establish the results in the first place.There we have a handle on the problem. We have a reasonable understanding of what kinds of methods are good for establishing what kinds of results and with what (at least rough) degrees of certainty.

There are methods – well thought through – that social scientists learn in the course of their training for constructing a questionnaire, running a randomised controlled trial, conducting an ethnographic study, looking for patterns in large data sets. There is nothing comparably explicit and well thought through about how to use social science knowledge to help predict what will happen when we implement a proposed policy in real, complex situations. Nor is there anything to help us estimate and balance the effectiveness, the evidence, the chances of success, the costs, the benefits, the winners and losers, and the social, moral, political and cultural acceptability of the policy.

To see why this is so difficult think of an analogy: not building social policies but building material technologies. We do not just read off instructions for building a laser – which may ultimately be used to operate on your eyes – from knowledge of basic science. Rather, we piece together a detailed model using heterogeneous knowledge from a mix of physics theories, from various branches of engineering, from experience of how specific materials behave, from the results of trial-and-error, etc. By analogy, building a successful social policy equally requires a mix of heterogeneous kinds of knowledge from radically different sources. Sometimes we are successful at doing this and some experts are very good at it in their own specific areas of expertise. But in both cases – both for material technology and for social technology – there is no well thought through, defensible guidance on how to do it: what are better and worse ways to proceed, what tools and information might be needed, and how to go about getting these. This is true whether we look for general advice that might be helpful across subject areas or advice geared to specific areas or specific kinds of problems. Though we indulge in social technology – indeed we can hardly avoid it – and are convinced that better social science will make for better policies, we do not know how to turn that conviction into a reality.

This presents a real challenge to the hopes for evidence-based policy….(More)”

Citizen Science in the Digital Age: Rhetoric, Science, and Public Engagement

Curated on January 24, 2017August 3, 2018 by Stefaan Verhulst

Book by James Wynn: “…highlights scientific studies grounded in publicly gathered data and probes the rhetoric these studies employ. Many of these endeavors, such as the widely used SETI@home project, simply draw on the processing power of participants’ home computers; others, like the protein-folding game FoldIt, ask users to take a more active role in solving scientific problems. In Citizen Science in the Digital Age: Rhetoric, Science, and Public Engagement, Wynn analyzes the discourse that enables these scientific ventures, as well as the difficulties that arise in communication between scientists and lay people and the potential for misuse of publicly gathered data.

Wynn puzzles out the intricacies of these exciting new research developments by focusing on various case studies. He explores the Safecast project, which originated from crowd-sourced mapping for Fukushima radiation dispersal, arguing that evolving technologies enable public volunteers to make concrete, sound, science-based arguments. Additionally, he considers the potential use of citizen science as a method of increasing the public’s identification with the scientific community, and contemplates how more collaborative rhetoric might deepen these opportunities for interaction and alignment. Furthermore, he examines ways in which the lived experience of volunteers may be integrated with expert scientific knowledge, and also how this same personal involvement can be used to further policy agendas.

Precious few texts explore the intersection of rhetoric, science, and the Internet. Citizen Science in the Digital Age fills this gap, offering a clear, intelligent overview of the topic intended for rhetoric and communication scholars as well as practitioners and administrators in a number of science-based disciplines. With the expanded availability of once inaccessible technologies and computing power to laypeople, the practice of citizen science will only continue to grow. This study offers insight into how—given prudent application and the clear articulation of common goals—citizen science might strengthen the relationships between scientists and laypeople….(More)”

Citizen Science and Crowdsourcing for Earth Observations: An Analysis of Stakeholder Opinions on the Present and Future

Curated on January 24, 2017August 3, 2018 by Stefaan Verhulst

Suvodeep Mazumdar, Stuart Wrigley and Fabio Ciravegna in Remote Sense: “The impact of Crowdsourcing and citizen science activities on academia, businesses, governance and society has been enormous. This is more prevalent today with citizens and communities collaborating with organizations, businesses and authorities to contribute in a variety of manners, starting from mere data providers to being key stakeholders in various decision-making processes. The “Crowdsourcing for observations from Satellites” project is a recently concluded study supported by demonstration projects funded by European Space Agency (ESA). The objective of the project was to investigate the different facets of how crowdsourcing and citizen science impact upon the validation, use and enhancement of Observations from Satellites (OS) products and services. This paper presents our findings in a stakeholder analysis activity involving participants who are experts in crowdsourcing, citizen science for Earth Observations. The activity identified three critical areas that needs attention by the community as well as provides suggestions to potentially help in addressing some of the challenges identified….(More)”.

Be the Change: Saving the World with Citizen Science

Curated on January 24, 2017August 3, 2018 by Stefaan Verhulst

Book by Chandra Clarke: “It’s so easy to be overwhelmed by everything that is wrong in the world. In 2010, there were 660,000 deaths from malaria. Dire predictions about climate change suggest that sea levels could rise enough to submerge both Los Angeles and London by 2100. Bees are dying, not by the thousands but by the millions.

But what can you do? You’re just one person, right? The good news is that you *can* do something.

It’s called citizen science, and it’s a way for ordinary people like you and me to do real, honest-to-goodness, help-answer-the-big-questions science.

This book introduces you to a world in which it is possible to go on a wildlife survey in a national park, install software on your computer to search for a cure for cancer, have your smartphone log the sound pollution in your city, transcribe ancient Greek scrolls, or sift through the dirt from a site where a mastodon died 11,000 years ago—even if you never finished high school….(More)”

Algorithmic Life

Curated on January 23, 2017August 3, 2018 by Stefaan Verhulst

Review of several books by Massimo Mazzotti at LARB: “…As a historian of science, I have been trained to think of algorithms as sets of instructions for solving certain problems — and so as neither glamorous nor threatening. Insert the correct input, follow the instructions, and voilà, the desired output. A typical example would be the mathematical formulas used since antiquity to calculate the position of a celestial body at a given time. In the case of a digital algorithm, the instructions need to be translated into a computer program — they must, in other words, be “mechanizable.” Understood in this way — as mechanizable instructions — algorithms were around long before the dawn of electronic computers. Not only were they implemented in mechanical calculating devices, they were used by humans who behaved in machine-like fashion. Indeed, in the pre-digital world, the very term “computer” referred to a human who performed calculations according to precise instructions — like the 200 women trained at the University of Pennsylvania to perform ballistic calculations during World War II. In her classic article “When Computers Were Women,” historian Jennifer Light recounts their long-forgotten story, which takes place right before those algorithmic procedures were automated by ENIAC, the first electronic general-purpose computer.

Terse definitions have now disappeared, however. We rarely use the word “algorithm” to refer solely to a set of instructions. Rather, the word now usually signifies a program running on a physical machine — as well as its effects on other systems. Algorithms have thus become agents, which is partly why they give rise to so many suggestive metaphors. Algorithms now do things. They determine important aspects of our social reality. They generate new forms of subjectivity and new social relationships. They are how a billion-plus people get where they’re going. They free us from sorting through multitudes of irrelevant results. They drive cars. They manufacture goods. They decide whether a client is creditworthy. They buy and sell stocks, thus shaping all-powerful financial markets. They can even be creative; indeed, according to engineer and author Christopher Steiner, they have already composed symphonies “as moving as those composed by Beethoven.”

Do they, perhaps, do too much? That’s certainly the opinion of a slew of popular books on the topic, with titles like Automate This: How Algorithms Took Over Our Markets, Our Jobs, and the World.

Algorithms have captured the scholarly imagination every bit as much as the popular one. Academics variously describe them as a new technology, a particular form of decision-making, the incarnation of a new epistemology, the carriers of a new ideology, and even as a veritable modern myth — a way of saying something, a type of speech that naturalizes beliefs and worldviews. In an article published in 2009 entitled “Power Through the Algorithm,” sociologist David Beer describes algorithms as expressions of a new rationality and form of social organization. He’s onto something fundamental that’s worth exploring further: scientific knowledge and machines are never just neutral instruments. They embody, express, and naturalize specific cultures — and shape how we live according to the assumptions and priorities of those cultures….(More)”