What is Crowdfunding?

Crowdfunding is a fundraising strategy that encourages individual members of some “crowd” or group, such as the public, to contribute money, often variable amounts of money, via the Internet, to produce a specific good or service. According to Lambert and Schwienbacher, crowdfunding is an activity involving “an open call, essentially through the Internet, for the provision of financial resources either in the form of a donation or in exchange for some form of reward and/or voting rights in order to support initiatives for specific purposes”. Lambert and Schwienbacher define crowdfunding as a kind of crowdsourcing. Like crowdsourcing, crowdfunding allows the “general public (or the crowd)”, as individual “consumers”, to voluntarily “provide input to the development of the product, in this case in the form of financial help”.

Crowdfunding websites allow their members to seek funding for their own initiatives, as well as fund the initiatives of other members. In this way, crowdfunding invites users to act as both fundraisers and funders. To seek funding from the crowd, members specify a fundraising goal, including information on who is seeking funding, how much money they need, what they need the money for, when they need the money, and what rewards (if any) funders will receive in exchange for their financial support. Rewards include rights to use or own what is produced by the initiative, such as the right to own a copy of a song produced by the initiative, or rights to use or own something that is an accessory production, such as ownership rights to a thank-you gift.

After a fundraising goal has been published, other members of the website can then review the current status of these fundraising initiatives. The current status of a fundraising initiative often includes information about who has currently given funding to the initiative, how much they gave, how much funding is still needed, and how much time is left to give it. By associating a funder with what they fund, funders are rewarded with social credit for what the initiative intends to produce with those funds. In this way, most crowdfunding websites minimally serve as markets that facilitate the exchange of money for reputations, and maximally serve as markets that exchange money for both reputational and material rewards.

There are different kinds of crowdfunding websites that offer different kinds of financing, including grants, equity, and loans, each with different kinds of rewards. Websites like Kickstarter allow users to crowdfund project grants in exchange for rewards determined and offered by the fundraiser, while websites like Kiva, allow users to crowdfund microloans in exchange for social kudos and eventually an interest-free repayment of the loan, which can then be recycled into other microloans. For our purposes, we will mainly consider crowdfunding grants, since grants are a common form of financing research.

Examples of Crowdfunding Research Grants

Kickstarter is a crowdfunding website that provides a platform “to fund creative ideas and ambitious endeavors”. It uses an “all-or-nothing” mechanism that allows funders to pledge money toward a project, but it only transfers money from the funders’ pledges if the project’s fundraising goal is met by a certain deadline. This is supposed to lower the risk of project failure because all projects that receive funding are fully funded. Since a project is less likely to fail due to inadequate funding, people, including funders, are more likely to realize the benefits of the project. Kickstarter is a platform for crowdfunding grants because funders retain ownership rights over what they produce, and only optionally provide rewards to funders for contributing specified levels of funding. Kickstarter funds a variety of creative projects, including research projects. However, it has rarely funded research projects.

By searching for the keywords “research”, “grant”, and “study”, it appears that Kickstarter has only successfully crowdfunded a few research grants, and most of this research involves multimedia deliverables. For example, Mary Maxwell successfully used Kickstarter to crowdfund her research on plastic pollution in the ocean. Fifty-five people contributed, in total $5,000, to finance Maxwell’s “voyage from Bermuda to the Azores, as well as the cost of plastic sediment analysis, fish tissue sampling, and marine debris density research.” To motivate donations, Mary relied on a mixture of material rewards and visual rhetoric. To engender sympathy from potential funders, she showed a picture of a deformed turtle whose shell had grown around a piece of plastic trash. To entice funders with material rewards, Maxwell offered “a handwritten postcard from the trip” to those who offered $25 or more; “a personal thank you letter and an autographed photograph taken on the trip” to those who offered $50 or more; “an autographed book of photos, journal entries, statistics and findings from the trip” to those who offered $200 or more; “a teaching kit consisting of photos, journal entries, statistics, findings from the trip, slide show or video footage, water or sediment sample, and suggestions of actions you can take to help solve the problem” to those who offered $500 or more; and “a weekend stay for two in luxurious hotel accommodations in San Francisco and a live presentation about the voyage, in addition to a teaching kit consisting of photos, journal entries, statistics, findings from the trip, slide show or video footage, water or sediment sample, and suggestions of actions you can take to help solve the problem” to those that offered $1,000 or more. In fact, 1 person donated $1000 or more, 2 people donated between $500 and $1000, 3 people donated between $200 and $500, and 18 people donated between $50 and $200, and 14 people donated between $25 and $50. One research question that we might ask is to what extent does the success of crowdfunding research grants depend on offering multimedia rewards like pictures, audio, and videos of that research. If the success of crowdfunding research grants significantly depends on offering multimedia rewards, then crowdfunding may not be a viable way finance research that does not lend itself to visualizations.

One relatively unique design of Kickstarter is how it handles social credit. Unlike many other crowdfunding platforms, Kickstarter does not publicly indicate the amount contributed by each funder, and so does not give more social credit to to those who give more money. Kickstarter only lists the usernames of the funders, links to funder profiles, and the dates on which they were funded. Presumably this is to protect the economic privacy of the donors; perhaps certain donors don’t want to disclose the magnitude of their wealth and generosity. This opens another research question for crowdfunding: Does the display of contribution amounts significantly influence the likelihood that a funder will decide to contribute money?

PledgeBank is another crowdfunding platform that has been used to fund research grants. Unlike Kickstarter, it does not collect money, or limit itself to financial pledges. Instead PledgeBank allows people to post conditional pledges “of the form ‘I will do something, if a certain number of people will help me do it.’”. For example, in order to crowdfund Myalgic encephalomyelitis research, Serena Blanchflower posted the following pledge to PledgeBank: “I will pledge £250 to MERGE, to fund much needed biomedical research into ME but only if 25 other people will give £10 each.” PledgeBank allows other members to sign the pledge, which thereby pledges them to meet the condition of the original pledge. If the requisite number of people sign a pledge, and agree to meet its condition, then PledgeBank notifies everyone that the pledge has passed, and they should follow through with their pledges. PledgeBank allows people to indicate whether or not they have acted on their pledges, but it does not attempt to enforce it. In particular, it has no mechanism for collecting or transfering pledged money. The PledgeBank website claims that they have “surveyed many of the money-based pledges that have succeeded, and found that payment rates vary from 50% to well over 150%, with three-quarters of people paying being typical.” Unlike Kickstarter, PledgeBank does not emphasize a total fundraising goal amount, but instead focuses on the total amount of participation. For example, Blanchflower’s pledge does not emphasize that she is looking for at £250 of donations and if that she would match it with £250. Instead, it emphasizes that she is looking for 25 people to participate by donating £10 each. This raises another practical research question for crowdfunding: Does emphasizing a total fundraising goal amount tend to lead to significantly more total donations than emphasizing total participation?

In thinking about how to crowdfund academic research, it may be helpful to think about how investigative journalism research has been crowdfunded. Crowdfunding platforms like Spot.Us demonstrate some success in crowdfunding research grants for investigative journalism. For example, Peter Byrne used Spot.Us to crowdfund his investigation into how some members of the Regents of the University of California have personally profited from how billions of dollars of public university monies are invested in private companies that they own.

Spot.Us allows users to both pitch stories as citizen journalists and fund pitched stories as media consumers. So far, Byrne has raised $5,037.00 of the requested $10,000 from 83 people. Byrne’s fundraising deadline is 8 days overdue, but unlike Kickstarter, Spot.Us does not use an all-or-nothing model of crowdfunding. Spot.Us allows fundraising journalists to decide to accept a lower amount of funding than what they had originally requested. However, Spot.Us only pays journalists after they have produced their stories.

Spot.Us is also different from Kickstarter in that it does not offer individual rewards to funders. It only offers individual rewards to media organizations that are willing to finance at least 50% of a news article. In that case, Spot.Us temporarily gives those corporations exclusive copyrights to the crowdfunded article. In order to receive funding, Spot.Us requires freelance journalists to sign an independent contractor agreement, which gives Spot.Us “the sole discretion to decide whether, when and how to publish the Article and may sell or license any rights to the Article.” The agreement also notes that “it is the intent of the Organization to eventually release the content of the Article under creative commons.” In this way, funders of an article expect to eventually have the right to read an article, and let others read it for free. The independent contractor agreement also grants Spot.Us the right of first publication, preventing the freelance journalist, within the first 90 days, from publishing the content without Spot.Us’s permission. Spot.Us also requires would-be journalists to work with a peer-reviewer that Spot.Us chooses (although it allows the reporter to reject the first two editors that Spot.Us selects). One major problem with Spot.Us’s crowdfunding mechanism is that it leaves open the possibility for adversarial organizations to slow down the publication of stories by purchasing a majority stake, as well as the possibility of Spot.Us to censor or heavily edit content that does not align with its interests.

Problems with Crowdfunding Research Grants

Currently, academic researchers can crowdfund their research by using one of the existing crowdfunding websites, such as Kickstarter, PledgeBank, or Spot.Us., but these crowdfunding mechanisms do not adequately address two general problems with crowdfunding research. The first general problem with crowdfunding research is that crowdfunding requires the crowd to have money to spend on research, but many poor and low-income people lack money to spend on research – not to mention access to the requisite computer technology, and so would be largely excluded from participating in crowdfunding. If the research agenda were significantly influenced by crowdfunding, poor and low-income people would be significantly excluded from influencing the research agenda. Crowdfunding, along these lines, would largely follow a research agenda biased for the research interests of those who already wield significant economic power.

The second general problem with crowdfunding research grants is that most people lack the expertise necessary to determine the value of certain forms of research. If non-experts cannot determine the value of certain forms of research, then they may fund research that yields relatively little value. This information asymmetry may not only lead to the inefficient funding of research, but it may also undermine the motivation of non-experts to fund certain forms of research. For example, if a person does not feel qualified to make certain funding decisions, then they may choose not to participate in making those and other funding decisions. In the extreme, non-experts, even those with money to burn, may decide not to participate at all in making any funding decisions, and so avoid crowdfunding research altogether.

Mechanisms for Crowdfunding Research Grants

To address these two general problems with crowdfunding research grants, we must redesign the crowdfunding mechanism itself. To allow more poor and low-income people to participate in crowdfunding, we must design a crowdfunding mechanism that provides poor and low-income people with money to spend on research grants. Since poor and low-income people lack this money, it must be gathered from other sources and redistributed to them.

The crowdfunding mechanism could allow government agencies, charitable organizations, and individuals to grant groups of people, including poor and low-income people, money to individually spend on research grants. To do this, the mechanism would need a way to allow people to create and join custom groups, and to be automatically included in demographic groups based on verified personal data. For example, the mechanism could allow an organization like the United Way to donate and $500,000 to the group of people who make less than $25,000 a year and who live within 50 miles of New Orleans, so that every person in this demographic would have some money to spend on research grants aimed at improving disaster relief for poor and low-income people. After the money was donated to the group as whole, the crowdfunding mechanism could divide the money evenly amongst all active members who’s profiles fit that demographic description. In this way, the crowdfunding mechanism would facilitate meta-grants, grants given to a group of people in order for its members to personally contribute money to other grants. By augmenting crowdfunding mechanisms with meta-grants, people who are directly affected by serious socio-economic problems, and who are currently excluded from participating in crowdfunding, would be able to directly make important decisions about how to spend scarce research monies.

To accurately distribute money to members of specific groups, the crowdfunding mechanism would also need a way to verify whether a user is a member of a specific demographic or user-generated group. For user-generated groups, authenticating membership is easy; users are members of all public groups they join, and administrators of private groups determine whether an applicant is a member. Determining group membership for demographic groups is more difficult. To determine whether a user is a member of a demographic group, the user must provide some personal information, and trusted third parties must infer demographic data from this personal information. For example, perhaps a trusted third party can verify the address of an applicant and estimate the income level of the member based on that address.

The crowdfunding mechanism would also need a way to redistribute monies if members become inactive, leave a group, or leave the crowdfunding service. This would prevent group monies from clogging up in the accounts of inactive users. One option would be to have a deadline for spending money contributed by a group, after which the money would be returned to the group account and redistributed to active members. If a user leaves a group or the crowdfunding service, their money would be automatically returned to the group account and redistributed to active members.

It is important that group money can only be spent on legitimate research grants, and not laundered through fake research grants, or through grants that give kickbacks to funders. To do this, crowdfunding mechanisms need to vet who can receive group money. Since the crowdfunding website cannot afford to do this by itself, it can use affiliated and trusted research organizations. For example, a university, college, library, museum, or archive could be a trusted research organization, and the crowdfunding website could allow those organizations to authenticate the researcher status of fundraiser. The research organization could confirm some minimal personal information of the researcher, including their name and address. To receive sure how to verify income level. Perhaps the verified home address of a member could be used to estimate income based on nearby property values.

To address the second general problem with crowdfunding research, we must design a crowdfunding mechanism that allows non-experts to delegate funding decisions to those they perceive as more expert. One way to do this is to allow members to entrust some of their money to other members that they perceived as having more expertise than themselves. This approach recognizes that a funder may not know much about a particular area of research, but they probably know someone who knows more about that themselves. If the crowdfunding mechanism allows members to delegate spending decisions to those they believe are more informed than themselves, then we can expect more informed and more efficient allocations of funding. For example, you may not be a physicist, but you may know a person who is good at math, so you may entrust them with $5 to decide on a physics research grant to fund. That person may in turn not know a physics major that they trust, so she may entrust the original $5 entrusted to her, as well as $10 of her own money to her physics major friend to decide on a physics research grant to fund.

So with this design, locally perceived experts can in turn entrust money to people they perceive as more expert than themselves. This process of entrusting money from those with less expertise to those with more expertise, continues from the local level and culminates in money that is entrusted with global experts. This mechanism of delegating money from non-experts to experts would allow non-experts to retain control of the money flow, but leverage the expertise of trusted experts. To do this, the crowdfunding mechanism needs a way for members to limit how their “entrusted” money can be spent by trustees. If those entrusted users become inactive, the money must be automatically returned to the person who entrusted it. As we can see each unit of money could have a trail of trust, starting from the original owner, who is the least expert, to the people he first entrusted his money with (the local experts), and finally the people he does not directly know, but that those he knows ultimately trust as most informed to make a funding decision (the global experts). This delegative model for grant monies leverages information assymetries to make more efficient funding decisions; it uses people’s knowledge that other people know more than they do to intelligently route the money from non-experts to local experts to global experts.

Republican House Whip Eric Cantor recently launched an online political campaign, YouCut,
for people to vote on federal budget cuts. According to an interview with Fox News anchor, Gretchen Van Susteren, Cantor promises to propose the budget cut with the most votes for “an up-or-down vote on the House floor the following week.” Cantor’s YouCut website claims that YouCut “allows you to vote, both online and on your cell phone, on spending cuts that you want to see the House enact.” If this is true, then YouCut would be an enormous advancement for participatory budgeting and direct democracy in America. But it’s not true because while YouCut is somewhat participatory, it is not democratic. YouCut is not designed for you to make decisions about what to cut; it’s designed for Eric Cantor and the Republican Party elite to decide what to cut. In other words, YouCut is really TheyCut.

There are two main reasons why YouCut is really TheyCut. Firstly, YouCut does not allow you to propose budget cuts. The agenda is set by Eric Cantor and the Republican Party elite. This means that the majority of Americans – including ordinary Republicans, Independents, Democrats, and others – cannot directly propose budget cut items for the YouCut community to vote on. Instead, budget cut items are privately culled, filtered, and vetted by Cantor and the Republican Party elite, and then put on the website. While many ordinary people may agree with Cantor’s selection of budget cut items on the ballot, the process systematically prevents the inclusion of budget cut items that go against the interests of Cantor and the Republican Party elite. Ordinary Republicans, Independents, Democrats, and others may approve of prudent budget cuts that conflict with the political and economic interests of Eric Cantor and the Republican elite. If Eric Cantor and the Republican elite censor these budget items from the ballot, we would never know it because there is no publicly available data to show rejected proposals. Clearly, Cantor’s YouCut has the potential to provide a very distorted image of American budget cut preferences.

The second main reason why YouCut is really TheyCut is that YouCut is vague and not legally binding. Cantor’s proposals are vague. They do not include the specific language he would propose as legislation. Moreover, even though Cantor promises to propose legislation that addresses a democratically selected budget cut, he is not legally obligated to do so. This legal wiggle room is afforded by the vagueness of the proposed budget cut item. While he suggests otherwise, Cantor has not entered into a contract with the American people to propose specific legislation because, in part, he is not asking people to vote on specific legislation.

There are other systematic problems with YouCut’s seemingly democratic design. It does not allow people to attach comments to the proposed budget cut items, thereby silencing useful criticism. It is also hosted on a Republican controlled website, thereby discouraging input from non-Republicans, and potentially biasing the sample of voters to Republican voters.

All of my criticism of YouCut may be written off as a misunderstanding of partisan politics – that I am expecting too much direct democracy from the Republican House Minority Whip. He is a professional partisan politician after all, and one who directly benefits from the status-quo of indirect democracy. Some might ask why I am so surprised to discover another partisan ploy to rally political support with populist rhetoric, even sophisticated populist rhetoric, like the use of online voting that pretends to give political power to ordinary people about how to balance the federal budget.

The problem with this line of criticism is not that it assumes a tendency for political corruption, but that it accepts it without considering creative alternatives, alternatives like allowing the public to propose and vote on all proposed budget cut items, or allowing the public to post comments on any proposed budget cut item. These creative alternatives are not that difficult to imagine or implement, but they do move us closer to systematically rejecting political corruption in the legislative process.

On YouCut, Eric Cantor and the Republican elite ask us to “vote on this page today for your priorities and together we can begin to change Washington’s culture of spending into a culture of savings.” I am asking us to seriously re-examine whether YouCut actually allows us to democratically decide on our budget priorities, or whether YouCut allows Eric Cantor and the Republican elite to use us to provide a veneer of democracy for their budget priorities.

Eric Cantor and the Republican Party elite are not only generating media buzz and populist excitement about their campaign, but they also get your email address or phone number, depending on how you vote. You may be thinking that you are merely voting for a budget cut, that government is finally listening to you. Actually, you are adding yourself to a political list that might be used for other partisan purposes.

While YouCut is not designed for democratic decision-making, it demonstrates how easy it would be to design a democratic decision-making website, one that gathers votes from the public and connects those votes to legislative action. Ironically, while YouCut may be designed to manipulate the public for private interests, it gives us reason to think that we could design a website that actually overcomes those private interests by democratically voting on public interests. Currently, YouCut is really TheyCut, but in the not so distant future, we could really have a YouCut.

is not something that has been and always will be. It is neither universal nor an abstraction. Rather, for Dewey, the public is a specifiable and discernible entity that is inextricable from its conditions of origin. More precisely, for Dewey, the public is an entity brought into being through issues for the purpose of contending with these issues in their current state and in anticipation of the future consequences of these issues. (DiSalvo, p.49)

DiSalvo emphasizes that Deweyan publics are “situated” around experienced issues, “multiple”, and “not exclusive to a particular class or social milieu” (DiSalvo, p.50)

He identifies two characteristic tactics for constructing a Deweyan public, namely tracing and projecting. According to DiSalvo, tracing “is the activity of revealing, of exposing the underlying structures, arguments, and assumptions of an issue” and involves making marks that “follow and record the presence and movement of an artifact, event, or idea” (DiSalvo, p.55). Projecting, on the other hand, “can be defined as the representation of a possible set of future consequences associated with an issue”, and involves making “apparent the possible consequences of an issue” (DiSalvo, p.52-53).

DiSalvo stresses that the tactic of projection is predictive, but not prescriptive. In this sense, projecting is about what will probably happen – not what should happen. If this notion of projection as prediction-without-prescription aligns with Dewey’s construction of the public, it is somewhat misaligned with the rhetorical dimensions of predictions. Rhetorically, predictions are all about prescriptions. For example, weather reports are often used to suggest or prescribe behavior changes, such as stay off the icy roads. Similarly, political election predictions, rhetorically serve as prescriptive voting signals (people don’t like to “waste” their votes on those predicted to lose). So while I understand the apparent emphasis on prediction-as-description and not prediction-as-prescription, I think that our conceptualization of the tactic of projection should be widened to include prediction-as-prescription.

One of DiSalvo’s most useful interpretations of Deweyan publics, is how he teleologically relates tracing and projecting to history and forecasting. According to DiSalvo, “tracing” and “projecting” are, in part, distinguished from “history” and “forecasting”, on the grounds that tracing and projecting are oriented toward the present – they are in some sense for-the-present, whereas the purpose of history and forecasting is oriented for-the-past and for-the-future, respectively. (DiSalvo, p. 58) So tracing is not just about digging up the past for a more comprehensive retrospective, but it is an attempt to clarify the living present.

While many historians, especially progressive ones, would think that their work is relevant to present issues, we can definitely understand the historical pursuit of a more comprehensive account of the past, or of the most comprehensive explanation of how we got here, with no major loose ends. I wonder if it is this intellectual bias for historical comprehension that points away from present issues. Tracing does not seem to share this intellectual bias for comprehension.

In my view, tracing, unlike history, is intellectually biased for helping to address the issue. For example, a history about an issue would be bad if it did not mention any major event that helped cause that issue. However, it is possible that a good trace of an issue does not mention any major event causing the issue, if what it does mention about the history of an issue proves useful to the public in dealing with that issue. In this sense, the tactic of tracing is judged on the concrete utility of the information it provides in helping the public address the issue, and not judged on whether the information helps the public fully understand the progression of the issue. In other words, knowing all the causes of an issue does not imply knowing how to address the causes in a useful way. History is biased for a more comprehensive explanation, whereas tracing is biased for a more useful explanation.

DiSalvo’s tactics provide a useful conceptual framework for designers in the digital humanities, especially those that deal with digital archives, to consider whether they are designing Deweyan publics or historical archives.

There is a definite tension with what a digital archive is – is it a public or private space, and if it is a public space, can it also be a Deweyan public? In other words, can a digital archive, not only be a public place to store and organize historical references, but also be a place useful to and situated within personal political issues? I tried to design the Petition Archives so that it was not just a public space for political history or a private place for prompting political results, but a public space situated in personal political issues.

In my view, the politicization of the digital archive seems quite natural, since it is a form of socially networked digital media. For many archivists, allowing the public to trace or project their issues into a digital archive is a dangerous proposition. From this perspective, orienting the archive toward the present may disrupt or corrupt the interpretation of the past or the future. For example, adding comments or public tags to a digital archive, while of serious interest to many curators, is also seen as a threat to the educational value of the archive. Perhaps those comments will distract or misinterpret the artifact on display, or perhaps the user-contributed tags will overwhelm the archive with navigational noise.

Exhibits – not archives – are supposed to offer safer places for tracing and projecting. The exhibit can take digital copies of items from the archive, and manipulate them without corrupting the archive. From a data preservation point of view, one can build multiple exhibits around the same archival data without staining the past with the present. However, from an educational point of view, exhibits with user generated content that attempt to trace the past or project the future still threaten to misinterpret the past, present, and future. It is assumed that curators, historians, and archivists are somehow supposed to protect the public from misinterpreting the data. They are supposed to filter out the non-sense, authorize the good stuff, and keep it around in case someone needs it. However, protecting the public from the archive and the archive from the public is not solely motivated by an interest in preserving public education. It also motivated by a need to preserve and accrue political power.

In particular, the state, and its institutional subordinates, have an enormous political and economic interest in filtering the archive and its exhibits so that it can continue to profit from nationalism and patriotism. These institutions often do this by sugar-coating the past and the future with curated propaganda. For example, consider the Donald W. Reynolds Museum and Education Center at Mount Vernon, which spends much of its time propping up the image of the slave-owner George Washington as a “man of character”. What would be the psychological and economic impacts if the majority of Americans stopped sucking on the sugar and started to seriously re-interpret our first president as a greedy slave-owner? What would be the meaning of the dollar bill or the State of Washington or Washington D.C., if the people of America suddenly believed that George Washington was not simply “a man of character” who privately despised slavery and then “lead by example” through the freeing of his slaves, as Mount Vernon would have us believe, but that Washington was a cruel man who, for the vast majority of his life economically profited from slavery and who at the end of his life only freed some of his slaves to protect his posterity (Martha’s slaves were practically his slaves too). What would happen if Americans began to believe that Washington was the politician who tried to bribe Joseph Whipple, Portsmouth’s Custom’s Collector, to kidnap Ona Judge Staines, one of his wife’s former slaves who had escaped to New Hampshire, and ship her back to him. Such are the political stakes with allowing the people’s voice to trace the issue of slavery in the digital museum and archive space.

But tools like Omeka are beginning to challenge this notion that archives are just a place for history by offering some means for tracing and projection. Firstly, Omeka is free and open to any public to create their own digital archives. Individuals, and not just authorized cultural institutions, have access to Omeka. People can start an Omeka archive, as I did with the Petition Archives, to trace or project political issues.

Secondly, Omeka has a plugin architecture which allows users to create social plugins based on external web services, such as IntenseDebate, which allow the public to discuss, rate, and review individual archive items. Omeka also offers a Contribution plugin, which allows users to contribute items to the archive, thereby helping to trace their own connection to politically charged historical events.

In conclusion, DiSalvo provides us with some of the theoretical framework to analyze digital humanities tools, such as Omeka. Designers now have an opportunity to ask: How do digital humanities tools trace and project the past and the future? How can we use the tactics of tracing and projecting to rethink, redesign, and reconstruct digital archives as Deweyan publics? And we are left with bigger questions. Are their other tactics for creating Deweyan publics? Are there any general best practices or conventions for tracing and projecting?

Sources:

• "Design and the Construction of Publics." Design Issues Vol. 25, No 1., 2009: 48-63.
• Omeka, http://omeka.org
• The Petition Archives, http://petitionarchives.org
• Ona Judge Staines: Escape from Washington, Evelyn Gerson, http://www.seacoastnh.com/blackhistory/ona.html

How would we actually allocate limited shared resources, like our pooled tax money or pooled charitable donations, if we actually were permitted to directly discuss, evaluate, and vote on those allocations? We don’t really know because instead of directly participating in collective-decision making processes to allocate our shared resources, most of us mainly rely on a relatively small group of corporate leaders from government, industry, and the nonprofit sector to make those decisions for us.

Part of the reason why we do not directly participate in collective-decision making processes to allocate our shared resources is because we lack the information infrastructure to do so. For example, our banking websites do not allow us to set up group accounts and collectively vote on how our money is spent. Our government websites, even at the local level, do not allow us to collectively propose, rate or review public budget items. There are many websites that allow us to directly donate funds to nonprofit organizations, but almost none allow us to vote on how those charitable funds, once collected, are spent.

In each case, we the stakeholders are mainly viewed as funders (investors, taxpayers, donors), but not as deciders (executives, legislators, philanthropists). We are largely viewed as a raw source of money and labor for the organizations that purport to represent us, not as the democratic leadership that designs the policies and signs the checks for those organizations. So we need to research whether there are any economically viable democratic alternatives, whether there are any democratic information models and interfaces that can incentivize more fair and efficient allocations of shared resources. Perhaps the status quo, with its veneer of representative democracy, is the most fair and efficient socio-technical infrastructure for allocating our shared resources, but we do not know; we have not experimentally explored many alternatives.

So in general, I’m interested in researching whether democratic media can be used to incentivize fair and efficient allocations of organizational monies. I’m interested in researching the fairness and efficiency of democratic resource allocation mechanisms, especially those that allocate organizational monies in the form of minigrants, and the informational factors that contribute to their fairness and efficiency.

Another major reason why we have not directly participated in collective decision-making processes to allocate our shared resources is because of general arguments against direct democracy, which we occasionally hear and incuriously accept, especially the argument that we cannot use direct democracy to solve our resource allocation problems because solutions require expertise which we lack, which we cannot or will not learn, or which we cannot or will not share. This argument assumes that the status quo has and makes better use of expertise than every potential model of direct democracy, and that the current corporate leadership have and use more expertise than we have or could use.

However, I am not convinced that our politicians and CEOs either 1) have or use expertise more efficiently than every other potential group of voters, or 2) that every model of directly democratic decision-making is bound to lack sufficient expertise and participation. For example, most of our corporate deciders are lawyers, businessmen, or community organizers – not universal policy experts. Like every human being, they have major gaps in knowledge and rely on others to advise them during decision making processes. So why shouldn’t the experts that advised our politicians and CEOs on particular issues also have direct influence on the decision-making process of those issues? Why must their expertise be filtered by some minority of lawyers, businessmen, and community organizers?

In fact, it seems quite feasible, and potentially more fair and efficient, to create an online space where the public, including experts from all walks of life, can directly propose, review, and vote on how to allocate organizational monies, especially in the context of community minigrants.

If a person does not feel like they have the time or expertise, or if they are unwilling or unable, to directly vote on every minigrant, they can temporarily delegate or “loan” their vote for specific types of grants to other users that they trust and perceive to be more expert on those types of grants than themselves. Moreover, by default, people could delegate their votes to the established corporate leadership (this would be a vote for the status quo and should be available).

There are several other design features which would make this model of direct democractic decision-making more robust, such as the ability to reclaim and recast your vote prior to the vote’s deadline, and the ability for those who are currently stewards of loaned votes, to be able to reloan those votes to even more trusted experts in their social networks, thereby creating chains of loaned votes. The stewards, or representatives, higher up in the chain should have the ability to retrieve and recast the votes they loaned to any steward lower down on the chain. Of course, the owner of each vote would be the highest point in the chain, and she would have the ability to reclaim and recast her vote at anytime prior to the vote’s deadline from any steward of her vote. With this model, I predict that votes would tend to be dynamically routed and loaned along a chain of users from the owners of the votes to the locally perceived experts to the globally perceived experts, thereby increasing the overall satisfaction with the allocation of organizational monies.

This system of chained vote loaning would preserve the possibility of direct democracy, while encouraging the information efficiencies of representative democracy. It would significantly mitigate the threat of inherent information asymmetries from interfering with informed decision making. It would encourage greater political participation and knowledge sharing. It would advance personal responsibility for political and economic outcomes. Perhaps, most importantly, if adopted more broadly by government and industry, it would radically undermine the economic and mental waste poured into political ad campaigns. Since everyone is an alternative candidate, it would be too expensive to demonize every alternative representative. Without as many central characters or simple teams to glorify and demonize, news companies would find it more difficult to profit from horse-race non-sense; they would instead have to focus more on the cost-benefit analysis of policy proposals; they would have to get back to the job of informing our democracy.

Unlike the accountability and transparency systems currently found in government legislatures, if the people decided that their putative global experts were in fact ill-informed pundits, our model of chained vote loaning would allow them to immediately redistribute their votes to other trusted global experts, thereby dynamically re-percolating power to more trustworthy representatives. With the proper information infrastructure, this could reasonably occur because the owner of each vote would be able to see who is currently representing her on each issue and how her vote is currently cast. If dissatisfied with her current representative, she would be able to reclaim her vote and recast it, or reloan it to another user that better represents her individual interests.

I have only offered an adumbrated account of my model for chained vote loaning, but one worth researching and experimentally testing, especially in the context of community minigrants. I am very interested in determining how chained vote loaning compares to traditional models of representation, such as collectively elected representatives or appointed representatives, with respect to stakeholder participation and satisfaction.

Finally, I am generally interested in how recommender systems and other forms of social computing can facilitate democratic decision making. To make informed decisions over a large set of proposals, we need a way to democratically discover proposals and potential representatives. If our collaborative filtering mechanisms only attempt to account for the preferences of uninformed users, they could potentially only suggest the adoption of proposals supported by similarly uninformed users, which could snowball into the general adoption of uninformed proposals. What is needed is a way to incorporate the preferences of representative experts, while ultimately honoring the preferences of uninformed users. With chained vote loaning, one option would be to incorporate the preferences of a vote owner with her chained representatives’ preferences.

If pressed for a somewhat short list of research interests, I’d provide the following:

• democratic media
• incentive-centered design
• social informatics
• information economics
• experimental economics
• social choice theory
• social finance
• economic sociology
• market design
• social computing
• recommender systems

The Y-axis represents the percentage of people within a family income level that lack insurance. For example, in 2008, 46.30% of Americans with no family income had no health insurance. It looks like the more money you have, the more likely you will be insured, and the less money you have, the less likely you will be insured. So rich men, women, and children are more likely to have health insurance than poor men, women, and children. Is this distribution of health coverage fair? Do poor men, women, or children need or deserve less health care coverage than their richer counterparts?

During the early 1990's females started to receive more doctorates in sociology than males.

In 1980, females started earning more Master's degrees in sociology than males.

At least since 1966, females have been earning more Bachelor's degrees in sociology than males.

Between 1975 and 1985, there was a precipitous drop in the number of Master’s and Bachelor’s degrees in sociology. The decline began with the end of the Vietnam War in 1975 and the beginning of the Second Cold War. The decline ended around 1985, the same year that Mikhail Gorbachev rose to power.

Did sociology’s decline have anything to do with the end of the Vietnam War? Were people relieved and less interested in fighting the war machine? Did the sociology’s prior popularity rely on the transgressions of the Vietnam War?

Did sociology’s rebound in 1985 have have anything to do with the professionalization of the female workforce during the 1980s? Did the climb in Master’s degrees correspond with females trying to enter professions that draw on sociological study, like social work?

As of 2006, females earn more college degrees in sociology than males:

• Doctorate (62% female)
• Masters (68% female)
• Bachelors (70% female)

Today, I finished my first day of THATCamp 2009.  I went to the Annotation session and presented the Image Annotation plugin for Omeka.  I also participated in the Text Mining session, where historians were supposed to discuss their text mining needs with the computer scientists.  I was really impressed with the SEASR project and hope to create an Omeka plugin that can use it to extract and classify time terms from the metadata of Omeka items.  In this way, Omeka  items can be temporally indexed and plotted on a timeline.