Grey Data

greɪ ˈdeɪtə

A term for data accumulated by an institution for operational purposes and does not fall under any traditional data protection policies.

Organizations across all sectors accumulate a massive amount of data just by virtue of operating alone, and universities are among such organizations. In a paper, Christine L. Borgman categorizes these as grey data and further suggested that universities should take a lead in demonstrating stewardship of these data, which include student applications, faculty dossier, registrar records, ID card data, security cameras, and many others.

“Some of these data are collected for mandatory reporting obligations such as enrollments, diversity, budgets, grants, and library collections. Many types of data about individuals are collected for operational and design purposes, whether for instruction, libraries, travel, health, or student services.” (Borgman, p. 380)

Grey data typically does not fall under traditional data protection policies such as Health Insurance Portability and Accountability Act (HIPAA), Family Educational Rights and Privacy Act (FERPA), or Institutional Review Boards. Consequently, there are a lot of debates about how to use (or misuse) them. Borgman points out that universities have been “exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters.” On top of this, for-profit companies “are besieging universities with requests for access to data or for partnerships to mine them.”

Recognizing both the value of data and the risks arising from the accumulation of grey data, Borgman proposes a model of Data Stewardship by drawing on the practices of data protection in the University of California which concern information security, data governance, and cyber risk.

This model is an example of a good Data Stewardship practice that the GovLab is advocating amidst the rise of public-private collaboration in leveraging data for public good.

The GovLab’s Data Stewards website presents the need for such practice as follows:

“With these new practices of data collaborations come the need to reimagine roles and responsibilities to steer the process of using private data, and the insights it can generate, to address some of society’s biggest questions and challenges: Data Stewards.

“Today, establishing and sustaining these new collaborative and accountable approaches requires significant and time-consuming effort and investment of resources for both data holders on the supply side, and institutions that represent the demand. By establishing Data Stewardship as a function, recognized within the private sector as a valued responsibility, the practice of Data Collaboratives can become more predictable, scaleable, sustainable and de-risked.”


Borgman, C. L. (2018). Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier. ArXiv.

Young, A. (2018, November 26). About the Data Stewards Network. Retrieved March 6, 2019, from

What if You Could Vote for President Like You Rate Uber Drivers?

Essay by Guru Madhavan and Charles Phelps: “…Some experimental studies have begun to offer insights into the benefits of making voting methods—and the very goals of voting—more expressive. In the 2007 French presidential election, for instance, people were offered the chance to participate in an experimental ballot that allowed them to use letter grades to evaluate the candidates just as professors evaluate students. This approach, called the “majority judgment,” provides a clear method to combine those grades into rankings or a final winner. But instead of merely selecting a winner, majority judgment conveys—with a greater degree of expressivity—the voters’ evaluations of their choices. In this experiment, people completed their ballots in about a minute, thus allaying potential concerns that a letter grading system was too complicated to use. What’s more, they seemed more enthusiastic about this method. Scholars Michel Balinski and Rida Laraki, who led this study, point out: “Indeed, one of the most effective arguments for persuading reluctant voters to participate was that the majority judgment allows fuller expression of opinion.”

Additional experiments with more expressive ballots have now been repeated across different countries and elections. According to a 2018 summary of these experiments by social choice theorist Annick Laruelle,  “While ranking all candidates appears to be difficult … participants enjoy the possibility of choosing a grade for each candidate … [and] ballots with three grades are preferred to those … with two grades.” Some participant comments are revealing, stating, “With this ballot we can at last vote with the heart,” or, “Voting with this ballot is a relief.” Voters, according to Laruelle, “Enjoyed the option of voting in favor of several candidates and were especially satisfied of being offered the opportunity to vote against candidates.”…

These opportunities for expression might increase public interest in (and engagement with) democratic decision making, encouraging more thoughtful candidate debates, more substantive election campaigns and advertisements, and richer use of opinion polling to help candidates shape their position statements (once they are aware that the public’s selection process has changed). One could even envision that the basis for funding election campaigns might evolve if funders focused on policy ideas rather than political allegiances and specific candidates. Changes such as these would ideally put the power back in the hands of the people, where it actually belongs in a democracy. These conjectures need to be tested and retested across contexts, ideally through field experiments that leverage research and expertise in engineering, social choice, and political and behavioral sciences.

Standard left-to-right political scales and the way we currently vote do not capture the true complexity of our evolving political identities and preferences. If voting is indeed the true instrument of democracy and much more than a repeated political ritual, it must allow for richer expression. Current methods seem to discourage public participation, the very nucleus of civic life. The essence of civility and democracy is not merely about providing issues and options to vote on but in enabling people to fully express their preferences. For a country founded on choice as its tenet, is it too much to ask for a little bit more choice in how we select our leaders? …(More)”.



A process of making datasets raw in three steps: reformatting, cleaning, and ungrounding (Denis and Goeta).

Hundreds of thousands of datasets are now made available via numerous channels from both public and private domains. Based on the stage of processing, these datasets can be categorized as either raw data or processed data. According to an Open Government Data principle, raw data (or primary data) “are published as collected at the source, with the finest possible level of granularity, not in aggregate or modified forms.” While processed data is data that has been through some sort of adulteration, categorization, codification, aggregation, and other similar processes.

A large amount of data that is made publicly available come in processed form. For example, population, trade, and budget data are often presented in aggregated forms, preventing researchers from understanding the underlying stories behind these data, such as the differences in patterns or trends when gender, location, or other variables come into factor. Therefore, a rawification process is oftentimes needed in order for a dataset to be useful for a more detailed, secondary and valuable analysis.

Jérôme Denis and Samuel Goëta define ‘rawification’ as a process of reformatting, cleaning, and ungrounding data in order to obtain a truly ‘raw’ datasets.

According to Denis and Goëta, reformatting data means making sure that data that has been opened can also be easily readable by the users. This is usually achieved by reformatting the data so that it can be read and manipulated by most processing programs. One of the most commonly used formats is CSV (Comma Separated Values).

The next step in a rawification process is cleaning. In this stage, cleaning means correcting mistakes within the datasets, which include but not limited to, redundancies and incoherence. In many cases, datasets can have multiple entries for the same item, for example ‘New York University’ and ‘NYU’ might be interpreted as two different entities or ‘the GovLab’ and ‘the Governance Lab’ might experience a similar issue. Cleaning helps address issues like this.

The final step in a rawification process is ungrounding, which means taking out any ties or links from previous data use. Such ties include color coding, comments, and subcategories. This way the datasets can be purely raw and free of all associations and bias.

Opening up data is a clear step for increasing public access to information held within institutions. However, in order to ensure the utility of that data for those accessing it, a rawification process will likely be necessary.

Additional resources:

  • Denis, J., & Goëta, S. (2017). Rawification and the careful generation of open government data. Social Studies of Science, 47(5), 604–629.
  • Denis, J., & Goëta, S. (2014). Exploration, Extraction and ‘Rawification’. The Shaping of Transparency in the Back Rooms of Open Data (SSRN Scholarly Paper No. ID 2403069). Rochester, NY: Social Science Research Network. Retrieved from

Data Fiduciary

ˈdeɪtə fəˈduʃiˌɛri

A person or a business that manages individual data in a trustworthy manner. Also ‘information fiduciary’, ‘data trust’, or ‘data steward’.

‘Fiduciary’ is an old concept in the legal world. Its latin origin is fidere, which means to trust. In the legal context, a fiduciary is usually a person that is trusted to make a decision on how to manage an asset or information, within constraints given by another person who owns such asset or information. Examples of a fiduciary relationship include homeowner and property manager, patient and doctor, or client and attorney. The latter having the ability to make decisions about the trusted asset that fall within the conditions agreed by the former.

Jack M. Balkin and Jonathan Zittrain wrote a case for “information fiduciary”, in which they pointed out the urgency of adopting the practice of fiduciary in the data space. In the Atlantic, they wrote:

“The information age has created new kinds of entities that have many of the trappings of fiduciaries—huge online businesses, like Facebook, Google, and Uber, that collect, analyze, and use our personal information—sometimes in our interests and sometimes not. Like older fiduciaries, these businesses have become virtually indispensable. Like older fiduciaries, these companies collect a lot of personal information that could be used to our detriment. And like older fiduciaries, these businesses enjoy a much greater ability to monitor our activities than we have to monitor theirs. As a result, many people who need these services often shrug their shoulders and decide to trust them. But the important question is whether these businesses, like older fiduciaries, have legal obligations to be trustworthy. The answer is that they should.”

Recent controversy involving Facebook data and Cambridge Analytica provides another reason for why companies collecting data from users need to act as a fiduciary. Within this framework, individuals would have a say over how and where their data can be used.

Another call for a form of data fiduciary comes from Google’s Sidewalk Labs project in Canada. After collecting data to inform urban planning in Quayside area in Toronto, Sidewalk Labs announced that they won’t be claiming ownership over the data that they collected and that the data should be “under the control of an independent Civic Data Trust.”

In a blog post, Sidewalk Labs wrote that:

“Sidewalk Labs believes an independent Civic Data Trust should become the steward of urban data collected in the physical environment. This Trust would approve and control the collection of, and manage access to, urban data originating in Quayside. The Civic Data Trust would be guided by a charter ensuring that urban data is collected and used in a way that is beneficial to the community, protects privacy, and spurs innovation and investment.”

Realizing the potential of creating new public value through an exchange of data, or data collaboratives, the GovLab “ is advancing the concept and practice of Data Stewardship to promote responsible data leadership that can address the challenges of the 21st century.” A Data Steward mirrors some of the responsibilities of a data fiduciary, in that she/he is “responsible for determining what, when, how and with whom to share private data for public good.”

Balkin and Zittrain suggest that there is an asymmetrical power between companies that collect user generated data and the users themselves, in that these companies are becoming indispensable and having more control over individuals data. However, these companies are currently not legally obligated to be trustworthy, meaning that there is no legal consequence for when they use this data in a way that breach privacy or in the least interest of the customers.

Under a data fiduciary framework, individuals who are trusted with data are attached with legal rights and responsibilities regarding the use of the data. In a case where a breach of trust happens, the trustee will have to face legal consequences.

More information:

Knowledge and Politics in Setting and Measuring SDGs

Special Issue of Global Policy: “The papers in this special issue investigate the politics that shaped the SDGs, the setting of the goals, the selection of the measurement methods. The SDGs ushered in a new era of ‘governance by indicators’ in global development. Goal setting and the use of numeric performance indicators have now become the method for negotiating a consensus vision of development and priority objectives.  The choice of indicators is seemingly a technical issue, but measurement methods interprets and reinterprets norms, carry value judgements, theoretical assumptions, and implicit political agendas.  As social scientists have long pointed out, reliance on indicators can distort social norms, frame hegemonic discourses, and reinforce power hierarchies. 

The case studies in this collection show the open multi-stakeholder negotiations helped craft more transformative and ambitious goals.  But across many goals, there was slippage in ambition when targets and indicators were selected.  The papers also highlight how the increasing role of big data and other non-traditional sources of data is altering data production, dissemination and use, and fundamentally altering the epistemology of information and knowledge.  This raises questions about ‘data for whom and for what’ – fundamental issues concerning the power of data to shape knowledge, the democratic governance of SDG indicators and of knowledge for development overall.


Knowledge and Politics in Setting and Measuring the SDGs – Sakiko Fukuda-Parr and Desmond McNeill 

Case Studies

The Contested Discourse of Sustainable Agriculture – Desmond McNeill 

Gender Equality and Women’s Empowerment: Feminist Mobilization for the SDGs – Gita Sen

The Many Meanings of Quality Education: Politics of Targets and Indicators in SDG4 – Elaine Unterhalter 

Power, Politics and Knowledge Claims: Sexual and Reproductive Health and Rights in the SDG Era – Alicia Ely Yamin 

Keeping Out Extreme Inequality from The SDG Agenda – The Politics of Indicators – Sakiko Fukuda-Parr 

The Design of Environmental Priorities in the SDGs – Mark Elder and Simon Høiberg Olsen 

The Framing of Sustainable Consumption and Production in SDG 12  – Des Gasper, Amod Shah and Sunil Tankha 

Measuring Access to Justice: Transformation and Technicality in SDG 16.3. – Margaret L. Satterthwaite and Sukti Dhital 

Data Governance

The IHME in the Shifting Landscape of Global Health Metrics – Manjari Mahajan

The Big (data) Bang: Opportunities and Challenges for Compiling SDG Indicators – Steve MacFeely …(More)”



“a new radical, practice-based ideology […] based on the values of sharing, common (intellectual) ownership and new social co-operations.”

Distinctive, yet with perhaps an interesting hint, from “communism”, the term “Commonism” was first coined by Tom DeWeese, the president of the American Policy Center yet more recently redefined in a new book “Commonism: A New Aesthetics of the Real” edited by Nico Dockx and Pascal Gielen

According to their introduction:

“After half a century of neoliberalism, a new radical, practice-based ideology is making its way from the margins: commonism, with an o in the middle. It is based on the values of sharing, common (intellectual) ownership and new social co-operations. Commoners assert that social relationships can replace money (contract) relationships. They advocate solidarity and they trust in peer-to-peer relationships to develop new ways of production.

“Commonism maps those new ideological thoughts. How do they work and, especially, what is their aesthetics? How do they shape the reality of our living together? Is there another, more just future imaginable through the commons? What strategies and what aesthetics do commoners adopt? This book explores this new political belief system, alternating between theoretical analysis, wild artistic speculation, inspiring art examples, almost empirical observations and critical reflection.”

In an interview excerpted from the book, author Gielen, Vrije Universiteit Brussel professor Sonja Lavaert, and the philosopher Antonio Negri discuss how commonism has the ability to transcend the ideological spectrum. The commons, regardless of political leanings, collaborate to “[re-appropriate] that of which they were robbed by capital.” Examples put forward in the interview include “liberal politicians write books about the importance of the basic income; neonationalism presents itself as a longing for social cohesion; religiously inspired political parties emphasize communion and the community, et cetera.”

In another piece, Louis Volont and Walter van Andel, both of the Culture Commons Quest Office, argue that an application of commonism can be found in blockchain. They argue that Blockchain’s attributes are capable of addressing the three elements of the tragedy of the commons, which are “overuse, (absence of) communication, and scale”. Further, its decentralization feature enables a “common” creation of value.

Although, the authors caution of a potential tragedy of blockchain by asserting that:

“But what would happen when that one thing that makes the world go around – money (be it virtual, be it actual) – enters the picture? One does not need to look far: many cryptocurrencies, Bitcoin among them, are facilitated by blockchain technology. Even though it is ‘horizontally organized’, ‘decentralized’ or ‘functioning beyond the market and the state’, the blockchain-facilitated experiment of virtual money relates to nothing more than exchange value. Indeed, the core question one should ask when speculating on the potentialities of the blockchain experiment, is whether it is put to use for exchange value on the one hand, or for use value on the other. The latter, still, is where the commons begin. The former (that is, the imperatives of capital and its incessant drive for accumulation through trade), is where the blockchain mutates from a solution to a tragedy, to a comedy in itself.”

Mechanistic Evidence

There has been mounting pressure on policymakers to adopt and expand the concept of evidence-based policy making (EBP).

In 2017, the U.S. Commission on Evidence-Based Policymaking issued a report calling for a future in which “rigorous evidence is created efficiently, as a routine part of government operations, and used to construct effective public policy.” The report asserts that modern technology and statistical methods, “combined with transparency and a strong legal framework, create the opportunity to use data for evidence building in ways that were not possible in the past.”

Similarly, the European Commission’s 2015 report on Strengthening Evidence Based Policy Making through Scientific Advice states that policymaking “requires robust evidence, impact assessment and adequate monitoring and evaluation,” emphasizing the notion that “sound scientific evidence is a key element of the policy-making process, and therefore science advice should be embedded at all levels of the European policymaking process.” That same year, the Commission’s Data4Policy program launched a call for contributions to support its research:

“If policy-making is ‘whatever government chooses to do or not to do’ (Th. Dye), then how do governments actually decide? Evidence-based policy-making is not a new answer to this question, but it is constantly challenging both policy-makers and scientists to sharpen their thinking, their tools and their responsiveness.”

Yet, while the importance and value of EBP is well established, the question of how to establish evidence is often answered by referring to randomized controlled trials (RCTs), cohort studies, or case reports. According to Caterina Marchionni and Samuli Reijula these answers overlook the important concept of mechanistic evidence.

Their paper takes a deeper dive into the differences between statistical and mechanistic evidence:

“It has recently been argued that successful evidence-based policy should rely on two kinds of evidence: statistical and mechanistic. The former is held to be evidence that a policy brings about the desired outcome, and the latter concerns how it does so.”

The paper further argues that in order to make effective decisions, policymakers must take both statistical and mechanistic evidence into account:

“… whereas statistical studies provide evidence that the policy variable, X, makes a difference to the policy outcome, Y, mechanistic evidence gives information about either the existence or the nature of a causal mechanism connecting the two; in other words, about the entities and activities mediating the XY relationship. Both types of evidence, it is argued, are required to establish causal claims, to design and interpret statistical trials, and to extrapolate experimental findings.”

Ultimately Marchionni and Reijula take a closer look at why introducing research methods that beyond RCTs is crucial for evidence-based policymaking:

“The evidence-based policy (EBP) movement urges policymakers to select policies on the basis of the best available evidence that they work. EBP utilizes evidence-ranking schemes to evaluate the quality of evidence in support of a given policy, which typically prioritize meta-analyses and randomized controlled trials (henceforth RCTs) over other evidence-generating methods.”

They go on to explain that mechanistic evidence has been placed “at the bottom of the evidence hierarchies,” while RCTs have been considered the “gold standard.”

Evidence Hierarchy — American Journal of Clinical Nutrition

However, the paper argues, mechanistic evidence is in fact as important as statistical evidence:

“… evidence-based policy nearly always involves predictions about the effectiveness of an intervention in populations other than those in which it has been tested. Such extrapolative inferences, it is argued, cannot be based exclusively on the statistical evidence produced by methods higher up in the hierarchies.”

Some further readings on mechanistic evidence:

Social Physics

Merriam-Webster: “Social Physics: The quantitative study of human societysocial statistics”

When the US government announced in 2012 that it would invest $200 million in research grants and infrastructure building for big data in 2012, Farnam Jahanian, chief of the National Science Foundation’s Computer and Information Science and Engineering Directorate, stated that “Big data” has the power to change scientific research from a hypothesis-driven field to one that’s data-driven”.  Using big data to provide more evidence based ways ways of understanding human behavior is the mission of Alex (Sandy)Pentland, director of MIT’s Human Dynamics Laboratory. Pentland’s latest book illustrates the potential of what he describes as “Social Physics”.

The term was initially developed by Adolphe Jacques Quetelet, the Belgian socioligist and mathematician who introduced statistical methods to the social sciences. Quetelet expanded his views to develop a social physics in his book “Sur l’homme sur le developpement de ses facultes, ou Essai de physique sociale”. Auguste Comte, who coined “sociology” adopted the term (in his Positive Philosophy Volume Social Physics) when he defined sociology as a study that was just as important as biology and chemistry.

According to Sandy Pentland Social Physics is about idea flow, the way human social networks spread ideas and transform those ideas into behaviors. His book consequently aims to “extends economic and political thinking by including not only competitive forces but also exchanges of ideas, information, social pressure, and social status in order to more fully explain human behavior… Only once we understand how social interactions work together with competitive forces can we hope to ensure stability and fairness in our hyperconnected, networked society.”

The launch of the book is accompanied with a website that connects several scholars and explains the term further: “How can we create organizations and governments that are cooperative, productive, and creative? These are the questions of social physics, and they are especially important right now, because of global competition, environmental challenges, and government failure. The engine that drives social physics is big data: the newly ubiquitous digital data that is becoming available about all aspects of human life. By using these data with to build a predictive, computational theory of human behavior we can hope to engineer better social systems.”

Also check out the video below:


Marina Gorbis, executive director of the Institute for the Future (IFTF),  released a book entitled The Nature of the Future: Dispatches from the Socialstructed World. According to the IFTF website, the book “offers an inspiring portrayal of how new technologies are giving individuals so much power to connect and share resources that networks of individuals—not big organizations—will solve a host of problems by reinventing business, education, medicine, banking, government, and scientific research.” In her review in the New York Journal of BooksGeri Spieler argues that, when focusing on the book’s central premise, Gorbis “breaks through to the reader as to what is important here: the future of a citizen-created world.”

In many ways, the book joins the growing literature on swarmswikinomicscommons-based and peer-to-peer production methods enabled by advances made in technology:

“Empowered by computing and communication technologies that have been steadily building village-like networks on a global scale, we are infusing more and more of our economic transactions with social connectedness….The new technologies are inherently social and personal. They help us create communities around interests, identities, and common personal challenges. They allow us to gain direct access to a worldwide community of others. And they take anonymity out of our economic transactions.”

Marina Gorbis subsequently describes the impact of these technologies on how we operate as “socialstructing”:

“We are moving away from the dominance of the depersonalized world of institutional production and creating a new economy around social connections and social rewards—a process I call socialstructing. … Not only is this new social economy bringing with it an unprecedented level of familiarity and connectedness to both our global and our local economic exchanges, but it is also changing every domain of our lives, from finance to education and health. It is rapidly ushering in a vast array of new opportunities for us to pursue our passions, create new types of businesses and charitable organizations, redefine the nature of work, and address a wide range of problems that the prevailing formal economy has neglected, if not caused.

Socialstructing is in fact enabling not only a new kind of global economy but a new kind of society, in which amplified individuals—individuals empowered with technologies and the collective intelligence of others in their social network—can take on many functions that previously only large organizations could perform, often more efficiently, at lower cost or no cost at all, and with much greater ease.”

Following a brief intro describing the social and technical drivers behind socialstructing the book describes its manifestation in finance, education, governance, science , and health.  In the chapter “governance beyond government”  the author advocates the creation of a revised “agora” modeled on the ancient Greek concept of participatory democracy. Of particular interest, the chapter describes and explains the legitimacy deficit of present-day political institutions and governmental structures:

“Political institutions are shaped by the social realities of their time and reflect the prevailing technological infrastructure, levels of knowledge, and citizen values. In ancient Athens, a small democratic state, it was possible to gather most citizens in an assembly or on a hill to practice a direct form of democracy, but in a country with millions of people this is nearly impossible. The US Constitution and governance structure emerged in the eighteenth century and were products of a Newtonian view of the universe….But while this framework of government  and society as machines worked reasonably well for several centuries, it is increasingly out of sync with today’s reality and level of knowledge.”

Building upon the deliberative polling process developed by Professor James Fishkin, director of the Center for Deliberative Democracy at Stanford University, the author proposes and develops four key elements behind the so-called socialstructed governance:

The chapter provides for an interesting introduction of the kind of new governance arrangements made feasible by increased computing power and the use of collaborative platforms. As with most literature on the subject, little attention however is paid to evidence on whether these new platforms contribute to a more legitimate and effective outcomes – a necessary next step to move away from “faith-based” discussions to more evidence based interventions.


Research featured in the New Scientists focuses on the impact of so-called “slacktivism”, or “low-cost, low-risk online activism”, on subsequent civic action.  A detailed analysis of  slacktivism was developed by Henrik Serup Christensen in his 2011 paper in First Monday where he defined the concept and its origin as follows:

“Slacktivism has become somewhat of a buzzword when it comes to demeaning the electronic versions of political participation. The origins of the term slacktivism is debated, but Fred Clark takes credit for using the term in 1995 in a seminar series held together with Dwight Ozard. However, they used it to shorten slacker activism, which refer to bottom up activities by young people to affect society on a small personal scale used. In their usage, the term had a positive connotation.

Today, the term is used in a more negative sense to belittle activities that do not express a full–blown political commitment. The concept generally refer to activities that are easily performed, but they are considered more effective in making the participants feel good about themselves than to achieve the stated political goals. Slacktivism can take other expressions, such as wearing political messages in various forms on your body or vehicle, joining Facebook groups, or taking part in short–term boycotts such as Buy Nothing Day or Earth Hour.”

The research featured in the New Scientist comprises work by Yu-Hao Lee and Gary Hsieh, both from Michigan State University, who analyzed the effects of slacktivism following  (using the description of the New Scientist)  “the Colorado cinema shootings in 2012, which had prompted wide debate over access to firearms. Hsieh’s team recruited 759 US participants from Amazon’s Mechanical Turk crowdsourcing marketplace and surveyed them for their position on gun control. They asked people if they would sign an e-petition to either ban assault rifles or expand access to guns. Some of the participants then had the opportunity to donate to a group that was pro or against gun control. Another group, including people from both sides of the gun debate, were asked to donate to an education charity.” Findings:

“We found that participants who signed the online petition were significantly more likely to donate money to a related charity, demonstrating a consistency effect. We also found that participants who did not sign the petition donated significantly more money to an unrelated charity , demonstrating a  moral balancing  effect. The results suggest that  exposure to an online activism influences individual decision on  subsequent civic actions.”

These two psychological effects provide additional insight on whether or not slacktivism is damaging real citizen engagement potentially replacing meaningful action – as suggested in the below UNICEF video – part of a series titled “Likes Don’t Save Lives”: