should open access journals charge for submissions?

source: The Scholarly Kitchen, “Should Open Access Journals Charge Submission Fees?” by Phil Davis

Would open access journals be better off if they charged submission fees? A recent report, “Submission Fees – A tool in the transition to open access?” by Mark Ware for Knowledge Exchange, provides a complex answer to a seemingly simple question: It depends.

By surveying the literature and conducting interviews with stakeholders (including publishers, editors, researchers, librarians, and funding agencies), Ware reports theoretical interest for implementing submission fees, although most players perceived few incentives to change the system and that such a transition was risky.

The clearest case for which submission fees might work was in open access journals with very high rejection rates. In this case, the additional income would be used to reduce article processing charges. Paradoxically, open access publishers were the least supportive of such a business model. The most supportive of submission fees were subscription-access publishers. Read More

COAR promotes open access to science

COAR

source: SISOB Project, “COAR: Promoting Open Access to Science”

Open Access (OA) to scientific publications is currently one of the key topics in the field of scientific research, and it has been approached from many different angles. As explained in the article “Open Access to Scientific Information  : “Open Access (…) may contribute to more effective dissemination of research and thus increase its impact”. COAR, which stands for Confederation of Open Access Repositories  is an association which works for promoting Open Access Repositories and thus improve the dissemination of scientific results.

COAR was launched in 2009 and now unites over 80 institutions in 24 countries from throughout Europe, Latin America, Asia and North America. Its mission is to “enhance greater visibility and application of research outputs through global networks of Open Access digital repositories”.

The COAR´s aims and objectives are becoming increasingly important because of the policies that are being implemented by official framework programmes for research. A good example is Horizon 2020 ; the UE Framework Programme for Research and Innovation (2014-2020) which defends Open Access to scientific results, especially those that emerge from publicly-funded research. The Spanish 14/2011 law on Science, Technology and Innovation follows similar lines; article 37 specifies procedures for open-access dissemination of research results.

Disseminating research results through Open Access is getting more and more common. According to the Directory of the Open Access Journals (DOAJ), nowadays there are more than 7500 Open Access journals, among them PLoS One ,  the Open Access “mega journal” which was launched in 2006. Furthermore, the Registry of Open Access Repositories (ROAR) reports that there are more than 2000 digital repositories of Open Access material.

The issue of Open Access has been generating much debate during scientific conferences and events. The SPARC Open Access Meeting, which was held in Kansas City, Missuouri on March 12-13, 2012, attracted over 200 participants who attended the event to discuss a host of Open Access topics including policy issues, author rights, Open Access publishing and repositories.

In this context, COAR takes a strong line. Here is what Norbert Lossau, the chair of COAR Executive Board said about the Elsevier´s case (http://www.coar-repositories.org/news/coar-writes-open-letter-as-reaction-to-elseviers-practices/): “We strongly believe that Open Access will greatly improve the impact and use of scholarly publications, and maximize our collective global investment in research”.

The COAR´s work can be divided into three broad areas: repository content, repository interoperability, and repository and repository networks support and training. Each of these areas is devoted to the issues which are crucial for the development of repositories: the content, the interoperability, and the repository networks, respectively.

The first area involves searching for and recommending the best sustainable practices for populating repositories. The second is devoted to developing COAR´s interoperability strategy in the repositories. This strategy was designed as a result of analyzing numerous repositories. The third one involves activities for supporting regional and national repository initiatives and promoting the profession of a repository manager.

Visit COAR on Twitter (@COAR_eV) and Facebook (COAR)

education experiment to change higher learning

image: Sam Comen

Sebastian Thrun and Peter Norvig in the basement of Thrun’s guesthouse, where they record class videos. Photo: Sam Comen

source: Wired, “The Stanford Education Experiment Could Change Higher Learning Forever” by Steven Leckart

Stanford doesn’t want me. I can say that because it’s a documented fact: I was once denied admission in writing. I took my last math class back in high school. Which probably explains why this quiz on how to get a computer to calculate an ideal itinerary is making my brain hurt. I’m staring at a crude map of Romania on my MacBook. Twenty cities are connected in a network of straight black lines. My goal is to determine the best route from Arad to Bucharest. A handful of search algorithms with names like breadth-first, depth-first, uniform-cost, and A* can be used. Each employs a different strategy for scanning the map and considering various paths. I’ve never heard of these algorithms or considered how a computer determines a route. But I’ll learn, because despite the utter lack of qualifications I just mentioned, I’m enrolled in CS221: Introduction to Artificial Intelligence, a graduate- level course taught by Stanford professors Sebastian Thrun and Peter Norvig.

Last fall, the university in the heart of Silicon Valley did something it had never done before: It opened up three classes, including CS221, to anyone with a web connection. Lectures and assignments—the same ones administered in the regular on-campus class—would be posted and auto-graded online each week. Midterms and finals would have strict deadlines. Stanford wouldn’t issue course credit to the non-matriculated students. But at the end of the term, students who completed a course would be awarded an official Statement of Accomplishment.

People around the world have gone crazy for this opportunity. Fully two-thirds of my 160,000 classmates live outside the US. There are students in 190 countries—from India and South Korea to New Zealand and the Republic of Azerbaijan. More than 100 volunteers have signed up to translate the lectures into 44 languages, including Bengali. In Iran, where YouTube is blocked, one student cloned the CS221 class website and—with the professors’ permission—began reposting the video files for 1,000 students. read more

hat tip to:

connecting more collaborative, meaningful

Carnegie Mellon is using social media to tap the collective intelligence of its student population. Classroom Salon uses annotations and comments to breathe life into an otherwise lackluster learning process. Now educators can monitor their students’ participation and learning styles, while those students gain the insight of their peers in real-time.

Access to Classroom Salon is currently limited to academic institutions by invitation only.

from hypothesis to data-driven research

source: Genome Engineering, “From hypothesis to data-driven research” by socrateslogos

As science becomes more complex, it is changing how we are looking at the drivers of research, and how we will cope with the vast volumes of data that are generated. While this review of the concept of data-driven research from the Leukippos Institute for Synthetic Biology looks at science in general, the ideas behind it are crucial for genomics and genetic research.

hypothesis-driven science

The basis for understanding nature is the question: What is reality? Some see matter as answer – this is materialism, a key concept underlying hypothesis-driven research.

The scientific process begins with guesswork, the hypothesis. We use reductionism and search for single correlations. The testing of a hypothesis is carried out by the deduction of an empirical consequence. An empirical consequence is a statement that follows logically from the hypothesis. It should be possible with the help of data to decide whether this statement is true or false.

image: Leukippos Institute

Next, we collect data. If the data are equal to the deduced empirical consequence, we conclude that the probability of the hypothesis increases. Otherwise, we conclude, that the probability of the hypothesis decreases.

If we have a hypothesis, we can then deduce an empirical consequence. There are two logical forms in the hypothesis-driven research:

image: Leukippos Institute

The first logical form is called modus tollens. If we do not observe data corresponding to the empirical consequence, we conclude that the hypothesis is not true.

The second logical form is called confirmed consequence. If our data conform to the empirical consequence we can’t necessarily conclude that the hypothesis is true – another hypothesis could have resulted in our empirical consequence. The latter method is generally the chosen one for our publications, because of the problems of publishing negative data.

Together, this leads to the crisis of the hypothesis-driven science. The hypothesis method can deal with simple correlations, but fails if the problem becomes more complex. Positive data are not useful for a logical valid argumentation. Negative data could lead to a logical valid statement, but these data do not support our publications and therefore our careers, so no real proof of the hypothesis is possible.

paradigm changes

We can observe a paradigm change in science, and two computer developments are responsible. The first is the enormous storage capacity in the cloud. The second is that a huge number of computers have been connected and organized in social networks.

These changes have resulted in huge quantities of data and complex systems, a problem normal science cannot solve. Groundbreaking theories have been published:

Notable further developments of systems theory are: Heinz Foerster’s second order-cybernetics, Ilya Prigogine’s work on self-organization and his systems theory concepts with thermodynamics, and Mitchell Feigenbaum’s work on chaos theory.

Systems theory is a fundament for software and thus influences the scientific method. Contemporary applications of systems theory are systems biology and synthetic biology. This philosophical movement has two components: Idealism and systems theory.

idealism

Idealism is based on Plato’s theory of forms (ideas). In line with this theory, cybernetics assumes that the human nervous system calculates reality. This means:

  • Our brain calculates a model of an object.
  • The human reception is the basis of the scientific method.
  • The key tools are mathematics and logic.
  • The result of this calculation is a model, which is not identical to the object.

Thus, it is impossible to achieve knowledge about the world such as it exists independent of us.

systems theory

In systems theory, contemporary science moves from reductionism to a more holistic position. A system is a set of interacting or independent components forming an integrated whole. Systems behavior involves input, processing and output of data, and can be self-organizing and self-regulating by feedback.

the concept of data-driven science

A data pool or database is the engine of the data-driven research approach.

image: Leukippos Institute

This uses a filter, an algorithm that determines which subset of data will be chosen from the data pool. The filter process results in a pattern, which is the system that we examine in our research. This pattern is subject to a scientific evaluation for example by experiments, and this pattern evaluation results in new data, which is placed back in the data pool as part of a feedback loop. Constant evaluation of the patterns in these networks dynamically fills the data pool and changes the pattern anatomy. A constant flow of data can be observed, and the process will be very fast, because of the large size of the network.

the logical structure of data-driven research

A data pool is a collection of facts, which can be true, false, probable, complete, or incomplete. The pattern is deduced from the data pool. Deduction is a logically true method, since it moves from general to specific.

image: Leukippos Institute

The logical form used in the data-driven method is called modus ponens. We define and apply a filter and this results in a pattern, and the pattern is as true as the input data from the data pool. Therefore – the quality of the data in the data pool is the key.

changing the scientific approach

The data-driven science approach changes the scientific method and results in a practice called science 2.0.

It is key that the data pool is as complete as possible. The input to the data pool must be quick and easy, and the output must be freely accessible. In addition, the data pool needs to be standardized, and be complete and held in common, with all the data in one pool.

Social networks will increase the levels of data input. We can use open lab notebooks, blogs with unlimited length and micro-formats such as tweets, and we will get, instead of papers in traditional journals, high-speed direct publishing to the data pool.

The demand for a complete, common, and standardized data pool means that negative and preliminary results are important. Moreover, papers need to be written in a computer-readable format.

We work with complex systems with dynamic feedback. We get dynamic publishing with preliminary results and post-publication review. A demand for a data pool with open access results in open access publishing and in open source licensees. Different filters will result in a range from robotic to art-driven science. The feedback will drive science at a previously unseen speed.

Einsteins of connectivity

image: Alex Nabaumsource: The Wall Street Journal, “The New Einsteins Will Be Scientists Who Share” by Michael Nielsen

In January 2009, a mathematician at Cambridge University named Tim Gowers decided to use his blog to run an unusual social experiment. He picked out a difficult mathematical problem and tried to solve it completely in the open, using his blog to post ideas and partial progress. He issued an open invitation for others to contribute their own ideas, hoping that many minds would be more powerful than one. He dubbed the experiment the Polymath Project.

Several hours after Mr. Gowers opened up his blog for discussion, a Canadian-Hungarian mathematician posted a comment. Fifteen minutes later, an Arizona high-school math teacher chimed in. Three minutes after that, the UCLA mathematician Terence Tao commented. The discussion ignited, and in just six weeks, the mathematical problem had been solved.

Other challenges have followed, and though the polymaths haven’t found solutions every time, they have pioneered a new approach to problem-solving. Their work is an example of the experiments in networked science that are now being done to study everything from galaxies to dinosaurs.

These projects use online tools as cognitive tools to amplify our collective intelligence. The tools are a way of connecting the right people to the right problems at the right time, activating what would otherwise be latent expertise.

Networked science has the potential to speed up dramatically the rate of discovery across all of science. We may well see the day-to-day process of scientific research change more fundamentally over the next few decades than over the past three centuries.

But there are major obstacles to realizing this goal. Though you might think that scientists would aggressively adopt new tools for discovery, they have been surprisingly inhibited. Ventures such as the Polymath Project remain the exception, not the rule.

Consider the idea of sharing scientific data online. The best-known example of this is the human genome project, whose data may be downloaded by anyone. When you read in the news that a certain gene is associated with a particular disease, you’re almost certainly seeing a discovery made possible by the project’s open-data policy.

Despite the value of open data, most labs make no systematic effort to share data with other scientists. As one biologist told me, he had been “sitting on [the] genome” for an entire species of life for more than a year. A whole species of life! Just imagine the vital discoveries that other scientists could have made if that genome had been uploaded to an online database.

Why don’t scientists share?

If you’re a scientist applying for a job or a grant, the biggest factor determining your success will be your record of scientific publications. If that record is stellar, you’ll do well. If not, you’ll have a problem. So you devote your working hours to tasks that will lead to papers in scientific journals.

Even if you personally think it would be far better for science as a whole if you carefully curated and shared your data online, that is time away from your “real” work of writing papers. Except in a few fields, sharing data is not something your peers will give you credit for doing.

There are other ways in which scientists are still backward in using online tools. Consider, for example, the open scientific wikis launched by a few brave pioneers in fields like quantum computing, string theory and genetics (a wiki allows the sharing and collaborative editing of an interlinked body of information, the best-known example being Wikipedia).

Specialized wikis could serve as up-to-date reference works on the latest research in a field, like rapidly evolving super-textbooks. They could include descriptions of major unsolved scientific problems and serve as a tool to find solutions.

But most such wikis have failed. They have the same problem as data sharing: Even if scientists believe in the value of contributing, they know that writing a single mediocre paper will do far more for their careers. The incentives are all wrong.

If networked science is to reach its potential, scientists will have to embrace and reward the open sharing of all forms of scientific knowledge, not just traditional journal publication. Networked science must be open science. But how to get there?

A good start would be for government grant agencies (like the National Institutes of Health and the National Science Foundation) to work with scientists to develop requirements for the open sharing of knowledge that is discovered with public support. Such policies have already helped to create open data sets like the one for the human genome. But they should be extended to require earlier and broader sharing. Grant agencies also should do more to encourage scientists to submit new kinds of evidence of their impact in their fields—not just papers!—as part of their applications for funding.

The scientific community itself needs to have an energetic, ongoing conversation about the value of these new tools. We have to overthrow the idea that it’s a diversion from “real” work when scientists conduct high-quality research in the open. Publicly funded science should be open science.

Improving the way that science is done means speeding us along in curing cancer, solving the problem of climate change and launching humanity permanently into space. It means fundamental insights into the human condition, into how the universe works and what it’s made of. It means discoveries not yet dreamt of.

In the years ahead, we have an astonishing opportunity to reinvent discovery itself. But to do so, we must first choose to create a scientific culture that embraces the open sharing of knowledge.

submission fees in open access journals

source: putting down a marker, “submission fees in open access journals” by Mark Ware

reportThe summary version of a report I wrote earlier this year for Knowledge Exchange on submission fees in open access journals has just been published on the KE website (see “report on submission fees” below).

Submission fees, in which an author pays a fee when submitting an article are already quite common in certain disciplines, notably economic and finance journals and in some areas of the experimental life sciences. The report found that that there could be benefits to publishers in certain cases (particularly for journals with high rejection rates) to switch to such a model. For high rejection rate journals one advantage would be that article processing charges could be kept much lower than they would otherwise have to be.

Overall there seems to be an interest in the model but the risks, particularly those involved in any transition, are seen by publishers to outweigh the perceived benefits. There is also a problem in that the advantages offered by submission fees are often general benefits that might improve the system but do not provide publishers and authors with direct incentives to change to open access. To support transition funders, institutions and publication funds could make it clear that submission fees would be an allowable cost. At present this is often unclear in their policies.

See also Open Access Submission Fees

Update 9/12/10: There’s a review and discussion of the report on the Scholarly Kitchen blog http://scholarlykitchen.sspnet.org/2010/12/09/open-access-submission-fees/

report on submission fees

Knowledge Exchange (KE) is examining different routes in achieving the vision of having a layer of scholarly and scientific content openly available in the internet. One of these routes involves exploring new developments in the future of publishing. Work is being undertaken investigating interesting alternative business models which could contribute to the transition to open access. In this light KE has commissioned a study investigating whether submission fees could play a role in a business model for Open Access journals.

The general conclusion of the report bearing the title ‘Submission Fees – a tool in the transition to open access?’ (see below), written by Mark Ware, is that there are benefits to publishers in certain cases to switch to a model in which an author pays a fee when submitting an article. Especially journals with a high rejection rate might be interested in combining submission fees with article processing charges in order to make the transition to open access easier. In certain disciplines, notably economic and finance journals and in some areas of the experimental life sciences, submission fees are already common.

Overall there seems to be an interest in the model but the risks, particularly those involved in any transition, are seen by the publishers to outweigh the perceived benefits. There is also a problem in that the advantages offered by submission fees are often general benefits that might improve the system but do not provide publishers and authors with direct incentives to change to open access. To support transition funders, institutions and publication funds could make it clear that submission fees would be an allowable cost. At present this is often unclear in their policies.

Author acceptance of submission fees is critical to its success. It is an observable fact that authors will accept them in some circumstances. Author acceptance would require further study though.

Based on the interviews and the modelling in the study one model in particular is regarded as the most suitable way to meet the current requirements (i.e. to strengthen open access to research publications). In this model authors pay a submission fee plus an Article Processing Fee and the article is subsequently made available in open access. Both fees are set at levels that balance acceptability with the author community with securing a meaningful mix of revenues for the Publisher.

submission Fees—a tool in the transition to open access?

scholarly communications must be scalable

text mosaicsource: Academic Evolution, “Scholarly Communications Must Be Scalable” by Gideon Burton

This is my eighth post in a series on how scholarly communications must transform. In this post, I explain that scholarship in the digital age must be scalable. As in my earlier post urging the integration of scholarship into the cyberinfrastructure, I am again pressing for scholars to recognize that the way their work is digitally mediated makes all the difference to its significance.

Scalability has become an absolutely necessary attribute for technological information systems today. I’m claiming that this trait is of equal importance for the information system that is scholarship. Here is the bottom line: As digital modes of communicating knowledge edge out the print-based publishing, any learned communication that is not made to scale will shrink in its audiences and relevance, whereas scholarship that embraces scalability will be far more dynamic, flexible, and responsive — a manifestly superior mode of knowledge.

So, what is scalability? In computer systems, scalability refers to whether a system can throttle data according to the dynamics of online demand. For example, Twitter, the microblogging system, stumbled early on because its infrastructure was not scaling. New users flooded Twitter faster than servers could be set up to handle them, and for a time Twitter was in danger of dying precisely because it was catching on. If it had not corrected for scaling, it would not be rising in use and importance. Businesses like Amazon and online services like those offered by Google have had to learn to scale to accommodate rapid growth. Must scholarship learn to scale, too?

Resistance to Scaling Up

Curiously, even though scholarly publishing is very much a business, and business most often thrives on increasing and meeting demand, accommodating demand is not what scholarly publishing is about. In fact, academic quality is in part measured by scholarly publishing NOT scaling up to any significant degree of demand.

For example, linguist Deborah Tannen faced the curious problem of losing face with her peers in the field of discourse analysis when her academic study (You Just Don’t Understand: Men and Women in Conversation) became a popular hit in the trade market. As soon as a scholar’s work moves from those $100 copies of cloth bound library monographs to $10 trade paperbacks, it isn’t seen as actually being serious scholarship anymore. Selling 200 to 2,000 copies of a scholarly monograph (mostly to libraries) is a scholarly success; selling 20,000 to 200,000 copies of a book removes it from the category of scholarship altogether. A popular title is something vulgar, tied more to commerce or the fleeting plaudits of celebrity than to intellectual rigor.

Sure, there are a few scholars like Oliver Sacks who manage to keep their mortar board and gain a broad following, but by and large, as academics see it, serious intellectual work is no longer serious if it scales up to significantly larger audiences.  Of course, every academic press would love to see sales of their scholarly books double. But if those sales went up by a power of 10 (much less 100), it would be as devastating to the press as losing its university underwriting. The authority of traditional scholarship is tied to its not scaling. Ironically, traditional scholarly publishing is a knowledge system that succeeds only if it fails — with the masses, that is.

Traditional scholarly publishing is just as resistant to scaling for production as it is for distribution. There is an ever increasing demand for scholars to publish, but academic journals must not scale up to meet that demand even if they had the manpower to do so. This is because not meeting demand for publication has become one of the main badges of scholarly quality.

Here’s how it works. If a journal turns away 99% of submissions, then the 1% that do get published must be all the more superior in quality. All of those rejected submissions — whose quantity or quality are never open for any public verification — somehow elevate the journal’s scholarly reputation by implying editorial rigor. In order for a journal’s or a press’s reputation to remain intact, it must be able to advertise an ongoing unmet demand for publication. Otherwise, what does end up getting published will lack the varnish coming from selectivity. Among academics evaluating one another’s publications, rejection rates for journals and presses are quoted with as much authority as journal Impact Factors. Like Impact Factor, and quite contrary to scientific principles, rejection rates are not verifiable. Similar to the black-box algorithm of Impact Factor, rejection rates only work in the dark.

The bottom line here is that traditional scholarly publishing is inherently opposed to scaling because the authority of scholarly products correlates to their scarcity — both how scarcely they are distributed to their special audiences, and how scarcely scholarly works are approved for publication. Traditional scholarship is scarcity knowledge — its authority is based on lots of knowledge NOT being published, lots of copies NOT being made, lots of audiences NOT being addressed, or money NOT being made. If any of those scarcity factors is displaced, the scholar’s or the journal’s reputation is threatened.

Universities whine at the high cost of subsidizing the publication of academic books, but if university presses were profitable, their editorial motives would be suspect. Scholars whine at the lack of publishing outlets for scholarship, but they turn around and use the very difficulty of getting published as hard currency in proving their own or others’ scholarly reputations. It makes one wonder at all the hand wringing that comes with the closing of another academic press; after all, press closures only increase the scarcity — and thus the value — of the surviving publications within this elite enterprise. No wonder the system is cracking; it not only can’t scale, but it won’t. To reach a broader public or to scale up the number of publications would undermine the exclusivity that only scarcity provides.  The scarcity-as-quality model is a terrible one for spreading or improving ideas (and terribly unnecessary today), but traditionalists can conceive of no other way. The ship may be listing hard to port, but most everyone stays on board since it has become the only acceptable passage to the safe harbor of authoritative academic knowledge.

Being married to the scarcity model of knowledge has resulted in many academics responding unfavorably to Open Access publishing and trembling at the very prospect of unchaperoned information. A recent Chronicle of Higher Education article uses fear mongering to question Open Access: “By making the products of research freely available to anyone,” worries Peter Schmidt, “it increases the risk that knowledge will fall into the hands of unintended audiences that could misuse it.” Opposition to Open Access sometimes takes this ethical approach, as though somehow if scholars have the self control not to turn loose shoddy ideas on the public, then the public will somehow follow suit.

But the information vacuum academics leave in the public sphere does not remain unfilled. When the public is teased by turning up search results to scholarly sources they cannot directly and fully access, they simply turn to those unvetted sources that are a click away. Scholars are learning the hard way that knowledge that is available for use becomes superior to the knowledge that is kept from use, regardless of any validating procedures that more restricted knowledge may have undergone. Fenced behind an access wall, such scholarship has a DO NOT USE ME sign on it, regardless of having passed rigorous peer review. This is a very strong reason why scholarship must be able to scale. Without intellectual liquidity, intellectual quality is a moot issue. If scholarship does not retool itself to scale up to the attention and active use of the masses, it is essentially making itself invisible and irrelevant. Closed knowledge systems are doomed in an online world whose most valued assets play to openness. Closed doesn’t scale.

Knowledge Improves with Circulation!

Scarcity and exclusiveness have bought a lot of prestige for scholars, but perhaps at the expense of that very knowledge that learned publishing was established to preserve and promote.

There is nothing more necessary for promoting the improvement of Philosophical Matters, than the communicating — [of] such things as are discovered or put in practice by others; it is therefore thought fit to employ the Press.  — Henry Oldenburg, 1665

So said the secretary of London’s Royal Society within the first English academic journal, Philosophical Transactions. A new medium had presented itself to that fledgling group of scientists. Print publication would leap past the limits of handwritten reports, speeding discovery, and broadening the number of interested parties who could extend, refute, and refine others’ work. Remnants of those original purposes remain in today’s system, but if Oldenburg were alive today he would wonder at our reluctance to embrace another medium promising a similar leap forward. The exponentially faster speeds of digital communication have put into high relief the fact that print publishing methods of knowledge production — even when used to produce electronic publications — impede that rapid flow and interchange of thought always accepted as a primary condition for the better evolution of ideas.

So ossified and sluggish is the reigning communications methods for scholars that they have ironically violated the primary article of faith for learned communication: the improvement and utility of knowledge correlates directly with how broadly, quickly, and interactively it circulates. People have been arguing for the free marketplace of ideas since the Enlightenment or earlier (Milton’s Areopagitica comes to mind). That marketplace is being reinvigorated through the Internet, and it’s poised for adding exponentially more value to ideas because of the new digital tools for intelligently finding, sifting, and gathering information. These tools include metadata, recommendation systems, social bookmarking, ranking algorithms, data harvesters, data mining, data visualization, simulations, virtualizations, telepresence, and hosts of others to come that have no real counterpart in the print world.

Knowledge online is smarter today, and getting smarter by the day. And that’s great news! Scholarship is able to scale today as never before because it can benefit from the robust searching, distributing, and sharing methods available to all digital content. Because of the vitally social nature of online communication, the digital realm can suddenly propagate mass attention to something through word of mouth. Of course this mass attention can go to a video clip of a cat swinging from a ceiling fan, but academic work CAN go viral. Anthropologist Michael Wesch can attest to this, with nearly 10 million views of his Web 2.0 video. Did this sudden, massive attention hurt Kansas State or Wesch’s own reputation? No, it put digital anthropology on the map in a major way and led to Wesch being recognized nationally for his teaching. His video was not scholarly publishing of any traditional variety, but he succeeded in moving the conversation forward in ways that a hundred peer reviewed articles about digitial anthropology never could. It also had a halo effect for Wesch’s more traditional publishing, his students, and his university.

But scholarship need not go viral for it to scale up meaningfully. Part of this simply means being prepared for bigger, more diverse audiences. Once scholars realize that their work is actually being read, commented upon, and actively integrated into current discussions, they may find it both profitable and enjoyable to ready their thinking for more than fellow specialists. Scaling up means scaling out to those audiences. This should change the way scholarship is conceived of, conducted, packaged, and valued.

The likelihood of a broadened audience for one’s intellectual work will naturally align scholarship more with civic involvement (see my post on scholars as public intellectuals), with real-world applications, and of course, with teaching. Many academic purists are above tainting their research with pedagogical or civic concerns, but universities fighting for the relevance of their intellectual products and purporting to promote teaching or life-long learning shouldn’t be turning a blind eye to ways that scholarship can be mediated to the masses. Scaling up scholarship means anticipating both the work and benefits attending a more diverse readership.

Obviously I’m talking about scalability in broader terms than simply a broadened distribution for scholarly work. Scalable scholarship will be that sort of communication readied to do and be more things in more places with more people than the dignified but static output of traditional publishing. Embracing scalability means rethinking rhetorical approaches, rethinking when and how one’s research circulates online, rethinking how we measure progress and contributions. It means being willing to scale outward to new forms of communication and collaboration. It means not keeping one’s scholarship scaled back to the tight borders of print-based communication or print-based ways of authoring or evaluating intellectual work.

Thus far I’ve only spoken abut scalability in terms of it scaling upward or outward, broadening its audiences, its scope, and its appeal. But it is important to note that scalability also refers to a system’s ability to down-throttle, to accommodate low loads. Serving low demand is just as vital to having a robust information system as gearing up for higher demand. This may seem paradoxical, but it’s all part of integrating scholarship — of the past, present, and future — into the Long Tail dynamics of online attention. This I will explain in my next post, Scholarly Communications must serve the Long Tail of Knowledge.

open academia

image: Research to Action

source: Bly, Adam, Kathleen Fitzpatrick and Katherine Rowe. (2010, September 22). Tea Party Online, Craigslist and Free Speech, and Open Academia. BrianLehrer.TV.

From the Brian Lehrer videocast (more transcript excerpts below): will the net kill the age-old system of peer review in academic publishing? knowledge: liberated and threatened by web publishing. sounds like common sense. publish-or-perish is the toughest part of being a professor; research and write and be OKd by experts in the field; peer review; elite journal is a must for top-job and tenure; it’s been the system for decades; the web is a disruptive force: publishing without a press, open peer review, show your work, in progress, publicly; what does this mean for the advancement of learning itself and for the politics of academia?

Scitable

scitable.com

Scitable, the data repository-slash-social network for aspiring scientists, might just be the greatest thing since sliced bread agarose gel. If you don’t get the analogy, you can search “agarose gel” on Scitable and in a few seconds you’ll have definitions, articles, and images illustrating the process of gel electrophoresis. Too easy? Maybe not. Once a thing of drudgery, knowledge acquisition is now the reward for following up a really cool thought with a few types and clicks.

Scitable makes learning so easy in fact, you might question the accuracy of the information. And you might feel skeptical to find that all of the site’s content was created using the same process used by the Nature journal; how can all of this information be free? There must be a catch, right?

Students and scientists alike know there are things more important than making a buck. We also understand how increasingly difficult it is to learn if you’re not a millionaire. Tell that to Nature Publishing Group (NPG)—the international academic publisher behind Nature and Scientific American that’s made a business out of making learning institutions students poor. NPG and publishers like it are the main reason tuition has increased by up to 600% in recent years; just read what George Monbiot has to say about it—the progressive journalist railed against the knowledge monopoly in this scathing op-ed. But while those of us passionate about learning have every reason to despise academic publishers, NPG seems to be making a comeback in the ethics race.

Scitable is defined by its social aspect. Members can connect with others interested in the same topic, and in real time. This networking dynamic makes the site feel more like a virtual classroom than your average wiki, partly because the profiles and groups that live there are inhabited by real people. The resulting community environment makes participating feel more relevant, more meaningful to students in developing countries who are otherwise unable to afford textbooks and other basics; Scitable membership is free.

Scitable, a collaborative learning space whose core principle is democratizing science education worldwide, is more evidence that revising current models of education can only lead to better quality in education—if we apply the right principles. Social networking is poised to make great waves of change, not because it’s the next big fad, but because it builds the potential of collective intelligence.