What does it cost to publish a Gold Open Access article?

“Folks, we have to have the vision to look beyond what is happening right now in our departments. Gold OA does, for sure, mean a small amount of short-term pain. It also means a massive long-term win for us all.”

Using social media and open access can radically improve the academic visibility of chapters in…

“None of this is to deny that if you have strong primary research to report it is better to push it out in journals wherever feasible. But book chapters can have valuable exploratory, discursive, synoptic and review roles. And they can carry new findings too, especially in start-up fields and with good editors and editing. The old problems from the early digital phase, when for a while chapter texts became literally unfindable, and authors passively left things to publishers to promote their work, no longer apply with much of their previous force. However conservative your editors and publishers may be, you can get your chapter noticed, read and cited in the communities that matter to you.”

Cambridge’s journey towards Open Access

“Presentation given at Open Repositories 2017, Brisbane, Australia. General track 13: Evaluation and assessment. This presentation discusses the open agenda supported by funder policies in the United Kingdom (UK), how these policies interact with one another and the resulting implications for higher education institutions using the case study of the University of Cambridge. The University of Cambridge has responded to the challenges of open research by founding the Office of Scholarly Communication and dedicating specialized teams to manage compliance with both Open Access and research data requirements. Since 2013 the Open Access Service has processed over 10,000 article submissions and spent more than £7 million on article processing charges. The experiences at Cambridge in responding to these challenges are an important lesson for anyone engaged in open research. This talk offers some insights into a potential way to manage funder mandates, but also acts as a cautionary tale for other countries and institutions considering introducing mandates around Open Access and what the implementation of certain policies might entail. The skills around management of open policies are significantly different to traditional library activity, and this has implications for training and recruitment of staff.”

 

Paying for Open Access – Knowledge Exchange

“To share a better understanding of author’s perspectives on APC payments, Knowledge Exchange has carried out a study among authors of six research organisations in the UK, France, Germany, Finland, Denmark, and the Netherlands. These organisations were very actively engaged which led to a total of 1069 authors participating in online surveys focused on their 2015 articles published in OA journals or in hybrid journals.”

Critical Data Literacy, why and how: an Open Education Resource (OER)

This OER was developed for presentation at the Data Power 2017 conference held at Carleton University, Ottawa, Ontario June 22 – 23. This is primarily a framework for how to go about teaching critical data literacy in the student-centered tradition of Freire, supplemented by the work of Tygel and colleagues. A sample introduction developed for Canadian university students, and a few references, are included. My definition of critical data literacy as used in this OER is: 

critical data literacy is the ability to understand and critique how the beliefs and values of people and groups (including government) influence what data is created, how it is shared and how it used by to tell compelling stories by storytellers whose beliefs and values shape the kind of stories they choose to tell and how they tell the stories. Critical data literacy also means having the ability to create and tell one’s own stories using data. 

This OER is released under the terms of copy and share – with love, my latest statement on sharing which can be found at the bottom of this post. The Freire tradition of popular education involves starting with the lived experience of students. In this context, following is what I recommend for anyone who wishes to develop a full critical data literacy program based on the framework. I think that this framework could be adapated for teaching at any level, from community-based learning (led by community groups or organizers or as a participatory action research project) to graduate classes (that’s where I teach). Some of the details would change. For example, if you are teaching at a university, some parts of the process are likely to involve formal evaluation (marking), but if you are teaching to the general public or a community group, this would not make sense. Please adjust as needed for your own context.

The overall approach:

  1. Identify your student group. Think about what kinds of issues or problems they might have that could potentially be helped by data, the kind of data stories they might be familiar with. 
  2. Develop an introduction to critical data literacy. Tygel and colleagues (2015, 2016) found that this was necessary. One way to think about the difference between critical data literacy and basic literacy (reading) is that people who do not know how to read in recent history are likely to be aware of the existence of reading as something that other people do. Data literacy / critical data literacy is not at this point in time as broadly understood as reading.
  3. Plan the 3 phases of the framework that follow directly from the Freire tradition: investigation, thematisation, and problematisation. In these phases, students should lead the learning process (active learning), pursuing problems and questions of their own devising. The teacher’s role is to provide support. 
  4. Plan a systematisation (synthesis) wrap-up approach that makes sense for your student group. In some cases this might be left for the students to decide the approach, and the teacher only helps to guide the students towards this closure. In a formal educational setting, this might involve a pre-determined assignment.
  5. Implement!

The 5 phases are: introduction, investigation, thematisation, problematisation, and systematization (synthesis). Details follow. The introduction section is the most fully developed as this is the only teaching portion that involves imparting knowledge; all others begin with the student.

Introduction

As noted above, it will not be obvious to everyone what data literacy or critical data literacy is or why they should learn about it, as discovered by Tygel and colleagues (2015, 2016). For this reason, an introduction to the topic may be helpful. In this phase one might invite in guest speakers from the community who use data in their storytelling and/or to provide examples of data storytelling. This is also where definitions of critical data literacy could be introduced. In addition to my definition (see above), I like this definition of data literacy from the Data Journalism Handbook  because it includes the element of critical thinking; not every definition that I have seen includes this, to me a significant omission.

data literacy is the ability to consume for knowledge, produce coherently and think critically about data [emphasis added] (Grey, Bounearu & Chambers (2012)

Following is a sample introduction developed for an audience of Canadian university students. If you are teaching a different type of student group, I recommend that you develop your own introduction tailored to your group. If you do and you are willing to share this with others, please send me a link (via e-mail to Heather dot Morrison at uottawa dot ca) or as a comment to this post and I will include a link to your work in this post. If you would like to use this introduction as is, please see the link to the full presentation.

Introduction slide 1

This slide presents two conflicting stories that are told using basically the same underlying data. One of these (tax freedom day) will be very familiar to the audience, while the other will not as it is relatively new. 

This slide illustrates two very different perspectives on taxation in Canada. On the left, we see the Fraser Institute’s Tax Freedom Day. The Fraser Institute, a right-wing think tank, uses data to tell their story of over-taxed Canadians, working more than half the year for the government before earning a dime for themselves. The idea of tax freedom day has been very effective in Canada over the past few decades. On the right, we see one of the images from the Broadbent Institute’s report The Brass Tax which was published very recently. The left-wing Broadbent Institute challenges the numbers behind the Fraser Institute’s analysis, argues that Canadian taxation is pretty reasonable compared to other countries, and presents a different picture. In this case this graph illustrates Canada’s progressive approach to taxation and makes the point that people with little to no income pay no income tax and only a small percentage of Canadians age 25 to 54 are in the top income tax bracket, paying more than 30% of income in taxes. These are 2 groups of people with a different vision of what society should be like, using the same underlying data to tell 2 very different stories. If we go directly to the data source, will this eliminate the impact of the storyteller? Let’s see.

The following two slides might be more effective as a live demo or in-class lab activity. 
 
One of the underlying datasets used by both groups is the statistics provided by OECD. If you go to the OECD website there are some neat online tools that let us quickly visualize data in different ways. One of the elements of the data story told by the Fraser Institute is that individual families pay too much in taxes. I wondered if there has been any change in the portion of tax revenue contributed through personal and corporate taxes over the years. Here is what I found using the OECD website. It seems that more tax is gathered from personal rather than corporate taxes, but over the past few years the portions don’t seem to have changed much. This is the default view that shows trends from 2000 – 2015. If this had fit what I already believed, I suspect I would have stopped here. But I seem to recall a relative decrease in corporate taxation over the past few decades so I decided to slide the years covered…
And this is what I found. If we slide the start date of the visualization tool back to 1965, it does appear that there has been a relative increase in tax revenue from personal sources and a relative decrease in tax revenue from corporate sources. This shows how easy it would be for two people with different perspectives on what a data trend is likely to be to go to exactly the same dataset and make a slight change to how the data is visualized to tell two very different stories. 

Kaulfuss uses OECD data to tell a story about U.S. health care spending on a blog called Beyond Economics. The story  is that the U.S. spends two and a half times the OECD average on health. It doesn’t surprise me that the U.S. spends more than the OECD average on health, but I am surprised that the difference is this much. What I found even more intriguing is the author’s claim that U.S. public spending on health is above the OECD average. Who knew? Disclaimer: what I am doing here is presenting stories told through data, I have not examined the data itself so cannot comment on the accuracy of the story.
 
Wikipedia has a section called Health Care in Canada. Here in Canada many of us – I include myself – think highly of our public health care system, and I think I see this perspective here. This section states that “most health statistics in Canada are at or above the G8 average” in a paragraph that is followed by the table pictured above. The table draws from a number of data sources and appears to me to demonstrate above-average data literacy skills. However…
 
When you look at the statistics that are presented and calculate the averages, Canada is above average on 3 of 8 measures. This is not “most”. This suggests a need for data literacy. If you look at the specific measures where we are above average, an argument can be made that being above average in life expectancy is a good thing. However, an above-average infant mortality rate is probably not such a good thing. We are also slightly above average on % of government revenue spent on health, but what does this mean and is it a good thing? Looking at some of the areas where we are below average –such as the  # of doctors & nurses per population & % of health costs paid by government – might give one reason to re-consider our narrative that we Canadians are above average in public health. This illustrates a need for critical data literacy. In other words, our beliefs might be getting in the way of understanding what is our existing data tells us.
Some approaches and suggestions  for creating a meaningful introduction     
The reason for the introduction section is because as Tygel and colleagues found there is a need to start with some explanation about what data is and how people use it. There are many potential approaches to introducing the topic such as having guest speakers come to explain how they make use of data and data visualization. 
Suggested sample activity
One activity that would fit here is to have students create their own demonstrations. In the case of tax data, students could do a google search for tax data and limit to images. This search will yield lots of material to work on. The idea is to have students find out who created the visualization and what the story behind the visualization is. If this is done for evaluation purposes, I recommend a pass/fail approach because student success will depend a lot on which images are selected. Being there to hear the findings of all the students is sufficient for this learning exercise. A teacher in an area where computers are not readily available could bring in copies of materials to work with. This introductory phase may be more relevant for some student groups than others, for example university students. If this doesn’t seem to fit, you could skip this stage. 

Investigation, Thematisation & Problematisation

Two key points to keep in mind in these 3 phases: 1) the core focus should be lived experience not imparting abstract knowledge and 2) teaching involves helping people seek and find answers. This is important because in teaching data literacy one might be tempted by starting with the data, teaching people how to understand and work with data. Keynote speaker Gwen Phillips (and BC First Nations data activist) at the Data Power 2017 provided a brilliant example of why not to start with the data: the existing data might not be what is wanted at all. As Gwen said, we should measure what do want (e.g. youth vitality) not just what we don’t want (e.g. teen suicide). This introduces a challenge to develop new metrics, but one that seems worthy of pursuit. If we start by teaching about existing data we risk missing the opportunity to identify gaps like this.

Disclosure: in understanding the following 3 phases, it may be helpful to know that although I teach at a university and am very engaged in pedagogy, I do not have an education degree and do not consider myself an expert on pedagogy. If you would like to know more about how to teach in the Freire tradition, I suggest starting with the Tygel references below and if desired supplementing with general educational books and articles covering the Freire tradition. My contributions below are limited to providing a very quick introduction and making the connection with critical data literacy.

Investigation

The investigation phase is the first of 3 phases that follow the Freire tradition. The idea is to begin with lived experience, with real-world problems. If this approach is used for self-teaching by community groups independently or with an academic consultant as a participatory action research project, this is closest to the classic Freire scenario and the best example of a pure investigation stage. To modify this for an education setting, students could either choose problems or issues of direct interest to them, for example student debt, or they might brainstorm a particular target group whose problems they are familiar with such as First Nations, a salient issue here in Canada as many of us struggle to implement the recommendations of our Truth & Reconciliation commission. Classroom activities could include a brainstorm session, individual or small group reflection, and/or presentation of the results of the investigation stage.
Thematisation
 
Thematisation is the first analytic stage. Before searching for what data is available, the idea is to focus on the real-world issue and figure out what kind of data might help to understand or resolve the issue. Examples based on today’s case studies on taxation and health spending could include learning what sorts of taxes are collected and by which governments, or comparing public collective health spending with individual spending.
 

Problematisation 

After thematisation, with some back-and-forth, comes problematisation. This is where we get into research on what kinds of data actually exist that is relevant to the problem, who collects the data and why. Some examples of the types of data sources students might look into at this point if they choose to focus on taxation and spending:

  • Canada Revenue Agency
  • OECD
  • Federation and provincial budgets
  • Academic Research 
  • NGO / Think Tank research (e.g. Fraser Institute and Broadbent Institute) 
One question that might be raised is whether the existing data is actually sufficient or not, that is, the scope of the inquiry is not focused just on understanding what data is available. but rather what is needed to understand and resolve the problem of interest. 
Systematization
 
Finally, in the systematization stage we put what we have together to come up with an action plan. The nature of the action plan might vary quite a bit depending on the students. An activist community group might want to develop an action campaign or an infographic or other data story to facilitate an existing action campaign. One approach to action could involve citizen data collection. In a graduate class on information policy, like the classes that I teach at the University of Ottawa’s School of Information Studies, developing a policy briefing and recommendations for evaluation as academic work might make sense. 
References

Fraser Institute (n.d.). Tax freedom day calculator. Retrieved June 9, 2017 from https://www.fraserinstitute.org/tax-freedom-day-calculator

Grey, J., Bounegru, L., & Chambers, L. (2012). Data Journalism Handbook. OKFN. (as cited in Tygel & Kirsch 2016)
Kaulfuss, R. (2017). Health care: human right or expensive entitlement? Beyond economics. Retrieved June 15, 2017 from  https://beyondeconomics.org/2017/03/15/health-care-human-right-or-expensive-entitlement/
OECD (2017), Tax revenue (indicator). doi: 10.1787/d98b8cf5-en (Accessed on 15 June 2017)
Shillington, R. & Shaban, R. (2017). The brass tax: busting myths about overtaxed Canadians. Ottawa: Broadbent Institute. Retrieved June 9, 2017 from http://www.broadbentinstitute.ca/the_brass_tax

Tygel, A.; Campos, M.; De Alvear, C. (2015). Teaching open data for social movements: a research strategy. The Journal of Community Informatics 11:3. Retrieved June 19, 2017 from http://ci-journal.net/index.php/ciej/article/view/1220/1165
 
Tygel, A.; Kirsch, R. (2016). Contributions of Paulo Freire for a critical data literacy: a popular education approach. The Journal of Community Informatics 12:3 pp. 108 – 121. Retrieved June 19, 2017 from http://ci-journal.net/index.php/ciej/article/view/1296.
Wikipedia (n.d.). Healthcare in Canada. Retrieved June 15, 2017 from https://en.wikipedia.org/wiki/Healthcare_in_Canada 

Terms:  Please copy and share with love.
What does this mean? In brief, I have no interest in using intellectual property law to prevent anyone from using or re-using my work with intentions such as furthering the collective knowledge of humanity (truth with justice and compassion), protecting or restoring the environment or making the conditions of life of humanity better. That is what I mean by with love. If your motives in using my work are something other than love, such as making a profit for yourself or a corporation that you work for, subverting truth, justice, or compassion, then note that I reserve all rights under copyright. Please use attribution as appropriate. For example, if you use my work in an academic or journalist context, you need to acknowledge me as author in order to avoid plagiarism (and confusion).

This post is part of the Creative Globalization series

Joseph Stiglitz, Knowledge as a Global Public Good (1999)

Abstract:  This chapter examines the worldwide trend toward increased privatization of both information and information and communications technologies, as well as the shrinking role of states and the effects on peoples’ access to adequate information goods and services. Publicness cannot be guaranteed unless users have low?cost access to the opportunities afforded by the new information technologies. Privatization of telecommunications carriers will not guarantee low?cost access and may actually impede it. In absolute terms, the prices of knowledge goods and services are higher in Africa than in high?income countries. In addition, even though information and communications technologies have public good attributes, they are embedded in power relationships. In particular, they are crucial for access to developing country markets, for intelligence purposes and for the transmission of ideas and ideologies. Most of all, they have the potential to widen the gap between haves and have?nots. Accordingly, the chapter calls for a renewed commitment to a public service agenda and, to that end, for increased cooperation between states on a regional basis.

Elsevier and CWTS release report on data sharing perceptions and practices among researchers

“To draw conclusions around data sharing practices among researchers, there was a need for an evidence base around research data management (RDM) attitudes and behavior. To address this need, Elsevier and the Centre for Science and Technology Studies (CWTS), part of Leiden University in the Netherlands, developed and published the report Open Data: The Researcher Perspective.

For the launch of the report at the Research Data Alliance 9th Plenary Meeting in Barcelona in April, Elsevier and CWTS brought together stakeholders from EU institutions, industry, academia and nonprofit organizations to discuss how the findings could be used to inform policy and translate into action.”