Combine the superfast calculational capacities of Big Compute with the oceans of specific personal information comprising Big Data — and the fertile ground for computational propaganda emerges. That’s how the small AI programs called bots can be unleashed into cyberspace to target and deliver misinformation exactly to the people who will be most vulnerable to it. These messages can be refined over and over again based on how well they perform (again in terms of clicks, likes and so on). Worst of all, all this can be done semiautonomously, allowing the targeted propaganda (like fake news stories or faked images) to spread like viruses through communities most vulnerable to their misinformation.
According to Bolsover and Howard, viewing computational propaganda only from a technical perspective would be a grave mistake. As they explain, seeing it just in terms of variables and algorithms “plays into the hands of those who create it, the platforms that serve it, and the firms that profit from it.”
Computational propaganda is a new thing. People just invented it. And they did so by realizing possibilities emerging from the intersection of new technologies (Big Compute, Big Data) and new behaviors those technologies allowed (social media). But the emphasis on behavior can’t be lost.
People are not machines. We do things for a whole lot of reasons including emotions of loss, anger, fear and longing. To combat computational propaganda’s potentially dangerous effects on democracy in a digital age, we will need to focus on both its howand its why.
until recently, broadcasting and publishing were difficult and expensive affairs, their infrastructures riddled with bottlenecks and concentrated in a few hands.
When protests broke out in Ferguson, Missouri, in August 2014, a single livestreamer named Mustafa Hussein reportedly garnered an audience comparable in size to CNN’s for a short while. If a Bosnian Croat war criminal drinks poison in a courtroom, all of Twitter knows about it in minutes.
In today’s networked environment, when anyone can broadcast live or post their thoughts to a social network, it would seem that censorship ought to be impossible. This should be the golden age of free speech.
And sure, it is a golden age of free speech—if you can believe your lying eyes. Is that footage you’re watching real? Was it really filmed where and when it says it was? Is it being shared by alt-right trolls or a swarm of Russian bots? My note: see the ability to create fake audio and video footage: https://blog.stcloudstate.edu/ims/2017/07/15/fake-news-and-video/
HERE’S HOW THIS golden age of speech actually works: In the 21st century, the capacity to spread ideas and reach an audience is no longer limited by access to expensive, centralized broadcasting infrastructure. It’s limited instead by one’s ability to garner and distribute attention. And right now, the flow of the world’s attention is structured, to a vast and overwhelming degree, by just a few digital platforms: Facebook, Google (which owns YouTube), and, to a lesser extent, Twitter.
at their core, their business is mundane: They’re ad brokers
They use massive surveillance of our behavior, online and off, to generate increasingly accurate, automated predictions of what advertisements we are most susceptible to and what content will keep us clicking, tapping, and scrolling down a bottomless feed.
in reality, posts are targeted and delivered privately, screen by screen by screen. Today’s phantom public sphere has been fragmented and submerged into billions of individual capillaries. Yes, mass discourse has become far easier for everyone to participate in—but it has simultaneously become a set of private conversations happening behind your back. Behind everyone’s backs.
It’s important to realize that, in using these dark posts, the Trump campaign wasn’t deviantly weaponizing an innocent tool. It was simply using Facebook exactly as it was designed to be used. The campaign did it cheaply, with Facebook staffers assisting right there in the office, as the tech company does for most large advertisers and political campaigns.
John Richard Schrock is Professor of Biology Emeritus at Emporia State University in Kansas. He is currently in China. While China is growing its universities, the U.S. is retreating from its historic commitment to make higher education accessible to all qualified students.
the elderly administrators soon retired. There was no supply of experienced junior administrators due to a Cultural Revolution that had closed many universities for a decade. That left China’s Ministry of Education with an opportunity to completely re-build its university system nationwide.
So by 1998, the situation was different. Weak universities were closed or merged with strong institutions. China doubled its university capacity, then doubled it again in the early 2000s, and doubled it again by 2010. The cities of Xi’an and Guangzhou built “university cities” with 10 new universities each. Chongqing built their “university city” with 17 different universities totaling 300,000 faculty, students and staff. –An area equivalent to the size of Wichita! -But all just universities. This was the greatest expansion of higher education in human history.
Now, the majority of their students who passed the gao kao high school leaving exam could now attend college. But students would now pay full tuition. And that greatly improved the faculty salaries and living conditions. Classrooms and labs soon became state-of-the-art.
In 1995, China selected over a hundred universities for its “211 Project,” feeding federal money toward building modern universities.
And as of two months ago, China began its Double World-Class Project. Their Ministry selected 42 universities to move to world-class status by 2050. 36 are Category A and 6 are Category B with a focus on applied research. It also has over 400 “key disciplines” spread across these and another 50 provincial universities that will receive additional generous governmental support.
Their National Natural Science Foundation announced a dramatic increase in grant funding two years ago. With a decade of substantial cash incentives for publishing in high ranked English journals, Chinese researchers have rapidly risen in authorship of research papers in the top science journals Science and Nature, second only to the U.S. in authorships. If this trend continues, China will be the top producer of research in a few more years.
For nearly four decades, China has invested in roads, railways, and other infrastructure. But the most important of these investments was education. Roads and rails move people around. Education moves people ahead. And it has paid off in raising the productivity of China’s population beyond expectations. The affluence of their institutions and the majority of their students reflect that payback. China understands that education is not just for filling those jobs needed today.
in France, four eggs “were placed before birth on top of drones while still inside the eggshell and, after hatching, (were kept) there during their early feeding period,” Reuters reported in November.
The eagles were named after characters in The Three Musketeers, and by February proved capable of intercepting drones in lightning-fast horizontal chases.
“Soon they will be casting off from peaks in the nearby Pyrenees Mountains,” AFP reported.
The military has already ordered a second brood of eagles, according to AFP.
Deadline: 15 February 2018 Open to: candidates with a Masters-level degree Benefits: full scholarship
Description
Finnish Government is pleased to offer a range of scholarships to students of high academic ability for 2018 entry. Students of Australia, China, Cuba, Egypt, Israel, Japan, Mexico, Mongolia, Namibia, Republic of Korea, Turkey, Ukraine and the USA are eligible to apply for these scholarships to undertake 3-9 months for doctoral level studies and research.
The Finnish Government Scholarships are available for 3-9 months for doctoral level studies and research at Finnish Universities or Public Research Institutes.
Eligibility
In order to be considered eligible to apply, you must fulfill all of the following criteria:
have established contact with the Finnish receiving institution before applying (see section ‘Doctoral Admissions’);
have a letter of invitation from the academic supervisor in Finland; the invitation should also explain the commitment of the host institution to the project;
have earned a Masters-level degree before applying;
intend to pursue post-master’s level studies as a visiting student, participate in a research project or teach at a university or public research institute in Finland; priority will be given to doctoral studies;
not have spent already more than one year at a Finnish higher education institution immediately before the intended scholarship period in Finland;
be able to give proof of sufficient skills in speaking and writing the language needed in study/research;
be a national of one of the eligible countries listed above.
Benefits
A monthly allowance of EUR 1500;
The allowance is sufficient for one person only;
Expenses due to travel, international or in Finland, are not covered by the program;
Scholarship recipients are recommended to make arrangements for sufficient insurance coverage for their stay in Finland;
Please see the section ‘Practical matters‘ for information on the practicalities of coming to Finland as an international student/researcher.
Application
Applications for the Finnish Government Scholarship Pool funding should be made to the appropriate authority in the applicant’s country. The scholarship authorities in each country are invited to present applications for up to 10 candidates for the Finnish Government Scholarship Pool. The announcements for the opening of the annual application round are usually sent out from CIMO at the end of September annually. Documents required for an application:
A completed and signed application form;
Curriculum vitae;
Copies of latest diplomas;
Two letters of recommendation;
Study/research plan (2-5 pages, including a statement of motivation, goals, work plan, work method, expected results);
Invitation/expression of interest and motivation for cooperation from the hosting academic supervisor in Finland;
Language certificate (Finnish, Swedish or English) or other indication of sufficient language skills – please see above, in the section ‘Eligibility criteria’.
The term “digital humanities” can refer to research and instruction that is about information technology or that uses IT. By applying technologies in new ways, the tools and methodologies of digital humanities open new avenues of inquiry and scholarly production. Digital humanities applies computational capabilities to humanistic questions, offering new pathways for scholars to conduct research and to create and publish scholarship. Digital humanities provides promising new channels for learners and will continue to influence the ways in which we think about and evolve technology toward better and more humanistic ends.
As defined by Johanna Drucker and colleagues at UCLA, the digital humanities is “work at the intersection of digital technology and humanities disciplines.” An EDUCAUSE/CNI working group framed the digital humanities as “the application and/or development of digital tools and resources to enable researchers to address questions and perform new types of analyses in the humanities disciplines,” and the NEH Office of Digital Humanities says digital humanities “explore how to harness new technology for thumanities research as well as those that study digital culture from a humanistic perspective.” Beyond blending the digital with the humanities, there is an intentionality about combining the two that defines it.
digital humanities can include
creating digital texts or data sets;
cleaning, organizing, and tagging those data sets;
applying computer-based methodologies to analyze them;
and making claims and creating visualizations that explain new findings from those analyses.
Scholars might reflect on
how the digital form of the data is organized,
how analysis is conducted/reproduced, and
how claims visualized in digital form may embody assumptions or biases.
Digital humanities can enrich pedagogy as well, such as when a student uses visualized data to study voter patterns or conducts data-driven analyses of works of literature.
Digital humanities usually involves work by teams in collaborative spaces or centers. Team members might include
researchers and faculty from multiple disciplines,
graduate students,
librarians,
instructional technologists,
data scientists and preservation experts,
technologists with expertise in critical computing and computing methods, and undergraduates
some disciplinary associations, including the Modern Language Association and the American HistoricalAssociation, have developed guidelines for evaluating digital proj- ects, many institutions have yet to define how work in digital humanities fits into considerations for tenure and promotion
Because large projects are often developed with external funding that is not readily replaced by institutional funds when the grant ends sustainability is a concern. Doing digital humanities well requires access to expertise in methodologies and tools such as GIS, mod- eling, programming, and data visualization that can be expensive for a single institution to obtain
Resistance to learning new tech- nologies can be another roadblock, as can the propensity of many humanists to resist working in teams. While some institutions have recognized the need for institutional infrastructure (computation and storage, equipment, software, and expertise), many have not yet incorporated such support into ongoing budgets.
Opportunities for undergraduate involvement in research, provid ing students with workplace skills such as data management, visualization, coding, and modeling. Digital humanities provides new insights into policy-making in areas such as social media, demo- graphics, and new means of engaging with popular culture and understanding past cultures. Evolution in this area will continue to build connections between the humanities and other disci- plines, cross-pollinating research and education in areas like med- icine and environmental studies. Insights about digital humanities itself will drive innovation in pedagogy and expand our conceptualization of classrooms and labs
1. Information security: Developing a risk-based security strategy that keeps pace with security threats and challenges.
2. Student success: Managing the system implementations and integrations that support multiple student success initiatives.
3. Institution-wide IT strategy: Repositioning or reinforcing the role of IT leadership as an integral strategic partner of institutional leadership in achieving institutions missions.
4. Data-enabled institutional culture: Using BI and analytics to inform the broad conversation and answer big questions.
5. Student-centered institution: Understanding and advancing technology’s role in defining the student experience on campus (from applicants to alumni).
6. Higher education affordability: Balancing and rightsizing IT priorities and budget to support IT-enabled institutional efficiencies and innovations in the context if institutional funding realities.
7. IT staffing and organizational models: Ensuring adequate staffing capacity and staff retention in the face of retirements, new sourcing models, growing external competition, rising salaries, and the demands of technology initiatives on both IT and non-IT staff.
8. (tie) Data management and governance: Implementing effective institutional data governance practices.
9. (tie) Digital integrations: Ensuring system interoperability, scalability, and extensibility, as well as data integrity, standards, and governance, across multiple applications and platforms.
10. Change leadership: Helping institutional constituents (including the IT staff) adapt to the increasing pace of technology change.
Please have also materials, which might help you organize our thoughts and expedite your Chapter 2 writing….
Do you agree with (did you use) the following observations:
The purpose of the review of the literature is to prove that no one has studied the gap in the knowledge outlined in Chapter 1. The subjects in the Review of Literature should have been introduced in the Background of the Problem in Chapter 1. Chapter 2 is not a textbook of subject matter loosely related to the subject of the study. Every research study that is mentioned should in some way bear upon the gap in the knowledge, and each study that is mentioned should end with the comment that the study did not collect data about the specific gap in the knowledge of the study as outlined in Chapter 1.
The review should be laid out in major sections introduced by organizational generalizations. An organizational generalization can be a subheading so long as the last sentence of the previous section introduces the reader to what the next section will contain. The purpose of this chapter is to cite major conclusions, findings, and methodological issues related to the gap in the knowledge from Chapter 1. It is written for knowledgeable peers from easily retrievable sources of the most recent issue possible.
Empirical literature published within the previous 5 years or less is reviewed to prove no mention of the specific gap in the knowledge that is the subject of the dissertation is in the body of knowledge. Common sense should prevail. Often, to provide a history of the research, it is necessary to cite studies older than 5 years. The object is to acquaint the reader with existing studies relative to the gap in the knowledge and describe who has done the work, when and where the research was completed, and what approaches were used for the methodology, instrumentation, statistical analyses, or all of these subjects.
If very little literature exists, the wise student will write, in effect, a several-paragraph book report by citing the purpose of the study, the methodology, the findings, and the conclusions. If there is an abundance of studies, cite only the most recent studies. Firmly establish the need for the study. Defend the methods and procedures by pointing out other relevant studies that implemented similar methodologies. It should be frequently pointed out to the reader why a particular study did not match the exact purpose of the dissertation.
The Review of Literature ends with a Conclusion that clearly states that, based on the review of the literature, the gap in the knowledge that is the subject of the study has not been studied. Remember that a “summary” is different from a “conclusion.” A Summary, the final main section, introduces the next chapter.
When conducting qualitative data, how many people should be interviewed? Is there a minimum or a max
Here is my take on it:
Simple question, not so simple answer.
It depends.
Generally, the number of respondents depends on the type of qualitative inquiry: case study methodology, phenomenological study, ethnographic study, or ethnomethodology. However, a rule of thumb is for scholars to achieve saturation point–that is the point in which no fresh information is uncovered in response to an issue that is of interest to the researcher.
If your qualitative method is designed to meet rigor and trustworthiness, thick, rich data is important. To achieve these principles you would need at least 12 interviews, ensuring your participants are the holders of knowledge in the area you intend to investigate. In grounded theory you could start with 12 and interview more if your data is not rich enough.
In IPA the norm tends to be 6 interviews.
You may check the sample size in peer reviewed qualitative publications in your field to find out about popular practice. In all depends on the research problem, choice of specific qualitative approach and theoretical framework, so the answer to your question will vary from few to few dozens.
How many interviews are needed in a qualitative research?
There are different views in literature and no one agreed to the exact number. Here I reviewed some mostly cited references. Based Creswell (2014), it is estimated that 16 participants will provide rich and detailed data. There are a couple of researchers agreed on 10–15 in-depth interviews are sufficient (Guest, Bunce & Johnson 2006; Baker & Edwards 2012).
your methodological choices need to reflect your ontological position and understanding of knowledge production, and that’s also where you can argue a strong case for smaller qualitative studies, as you say. This is not only a problem for certain subjects, I think it’s a problem in certain departments or journals across the board of social science research, as it’s a question of academic culture.
here more serious literature and research (in case you need to cite in Chapter 3)
Sample Size and Saturation in PhD Studies Using Qualitative Interviews
Gaskell, George (2000). Individual and Group Interviewing. In Martin W. Bauer & George Gaskell (Eds.), Qualitative Researching With Text, Image and Sound. A Practical Handbook (pp. 38-56). London: SAGE Publications.
Savolainen, Jukka 1994: “The Rationality of Drawing Big Conclusions Based on Small Samples.” Social Forces 72:1217-24. (http://www.jstor.org/pss/2580299).
Small, M.(2009) ‘How many cases do I need ? On science and the logic of case selection in field-based research’ Ethnography 10(1) 5-38
Williams,M. (2000) ‘Interpretivism and generalisation ‘ Sociology 34(2) 209-224
where you have several documents from the Graduate school and myself to start building your understanding and vocabulary regarding your quantitative, qualitative or mixed method research.
It has been agreed that before you go to the Statistical Center (Randy Kolb), it is wise to be prepared and understand the terminology as well as the basics of the research methods.
Please have an additional list of materials available through the SCSU library and the Internet. They can help you further with building a robust foundation to lead your research:
Books on intro to stat modeling available at the library. I understand the major pain borrowing books from the SCSU library can constitute, but you can use the titles and the authors and see if you can borrow them from your local public library
I also sought and shared with you “visual” explanations of the basics terms and concepts. Once you start looking at those, you should be able to further research (e.g. YouTube) and find suitable sources for your learning style.
I (and the future cohorts) will deeply appreciate if you remember to share those “suitable sources for your learning style” either by sharing in this Google Group thread and/or sharing in the comments section of the blog entry: https://blog.stcloudstate.edu/ims/2017/07/10/intro-to-stat-modeling. Your Facebook group page is also a good place to discuss among ourselves best practices to learn and use research methods for your chapter 3.
Watching the video, you may remember the same #BooleanSearch techniques from our BI (bibliography instruction) session of last semester.
Considering the fact of preponderance of information in 2017: your Chapter 2 is NOT ONLY about finding information regrading your topic.
Your Chapter 2 is about proving your extensive research of the existing literature.
The techniques presented in the short video will arm you with methods to dig deeper and look further.
If you would like to do a decent job exploring all corners of the vast area called Internet, please consider other search engines similar to Google Scholar:
Applications for the 2018 Institute will be accepted between December 1, 2017 and January 27, 2018. Scholars accepted to the program will be notified in early March 2018.
Title:
Learning to Harness Big Data in an Academic Library
Abstract (200)
Research on Big Data per se, as well as on the importance and organization of the process of Big Data collection and analysis, is well underway. The complexity of the process comprising “Big Data,” however, deprives organizations of ubiquitous “blue print.” The planning, structuring, administration and execution of the process of adopting Big Data in an organization, being that a corporate one or an educational one, remains an elusive one. No less elusive is the adoption of the Big Data practices among libraries themselves. Seeking the commonalities and differences in the adoption of Big Data practices among libraries may be a suitable start to help libraries transition to the adoption of Big Data and restructuring organizational and daily activities based on Big Data decisions. Introduction to the problem. Limitations
The redefinition of humanities scholarship has received major attention in higher education. The advent of digital humanities challenges aspects of academic librarianship. Data literacy is a critical need for digital humanities in academia. The March 2016 Library Juice Academy Webinar led by John Russel exemplifies the efforts to help librarians become versed in obtaining programming skills, and respectively, handling data. Those are first steps on a rather long path of building a robust infrastructure to collect, analyze, and interpret data intelligently, so it can be utilized to restructure daily and strategic activities. Since the phenomenon of Big Data is young, there is a lack of blueprints on the organization of such infrastructure. A collection and sharing of best practices is an efficient approach to establishing a feasible plan for setting a library infrastructure for collection, analysis, and implementation of Big Data.
Limitations. This research can only organize the results from the responses of librarians and research into how libraries present themselves to the world in this arena. It may be able to make some rudimentary recommendations. However, based on each library’s specific goals and tasks, further research and work will be needed.
Big Data is becoming an omnipresent term. It is widespread among different disciplines in academia (De Mauro, Greco, & Grimaldi, 2016). This leads to “inconsistency in meanings and necessity for formal definitions” (De Mauro et al, 2016, p. 122). Similarly, to De Mauro et al (2016), Hashem, Yaqoob, Anuar, Mokhtar, Gani and Ullah Khan (2015) seek standardization of definitions. The main connected “themes” of this phenomenon must be identified and the connections to Library Science must be sought. A prerequisite for a comprehensive definition is the identification of Big Data methods. Bughin, Chui, Manyika (2011), Chen et al. (2012) and De Mauro et al (2015) single out the methods to complete the process of building a comprehensive definition.
In conjunction with identifying the methods, volume, velocity, and variety, as defined by Laney (2001), are the three properties of Big Data accepted across the literature. Daniel (2015) defines three stages in big data: collection, analysis, and visualization. According to Daniel, (2015), Big Data in higher education “connotes the interpretation of a wide range of administrative and operational data” (p. 910) and according to Hilbert (2013), as cited in Daniel (2015), Big Data “delivers a cost-effective prospect to improve decision making” (p. 911).
The importance of understanding the process of Big Data analytics is well understood in academic libraries. An example of such “administrative and operational” use for cost-effective improvement of decision making are the Finch & Flenner (2016) and Eaton (2017) case studies of the use of data visualization to assess an academic library collection and restructure the acquisition process. Sugimoto, Ding & Thelwall (2012) call for the discussion of Big Data for libraries. According to the 2017 NMC Horizon Report “Big Data has become a major focus of academic and research libraries due to the rapid evolution of data mining technologies and the proliferation of data sources like mobile devices and social media” (Adams, Becker, et al., 2017, p. 38).
Power (2014) elaborates on the complexity of Big Data in regard to decision-making and offers ideas for organizations on building a system to deal with Big Data. As explained by Boyd and Crawford (2012) and cited in De Mauro et al (2016), there is a danger of a new digital divide among organizations with different access and ability to process data. Moreover, Big Data impacts current organizational entities in their ability to reconsider their structure and organization. The complexity of institutions’ performance under the impact of Big Data is further complicated by the change of human behavior, because, arguably, Big Data affects human behavior itself (Schroeder, 2014).
De Mauro et al (2015) touch on the impact of Dig Data on libraries. The reorganization of academic libraries considering Big Data and the handling of Big Data by libraries is in a close conjunction with the reorganization of the entire campus and the handling of Big Data by the educational institution. In additional to the disruption posed by the Big Data phenomenon, higher education is facing global changes of economic, technological, social, and educational character. Daniel (2015) uses a chart to illustrate the complexity of these global trends. Parallel to the Big Data developments in America and Asia, the European Union is offering access to an EU open data portal (https://data.europa.eu/euodp/home ). Moreover, the Association of European Research Libraries expects under the H2020 program to increase “the digitization of cultural heritage, digital preservation, research data sharing, open access policies and the interoperability of research infrastructures” (Reilly, 2013).
The challenges posed by Big Data to human and social behavior (Schroeder, 2014) are no less significant to the impact of Big Data on learning. Cohen, Dolan, Dunlap, Hellerstein, & Welton (2009) propose a road map for “more conservative organizations” (p. 1492) to overcome their reservations and/or inability to handle Big Data and adopt a practical approach to the complexity of Big Data. Two Chinese researchers assert deep learning as the “set of machine learning techniques that learn multiple levels of representation in deep architectures (Chen & Lin, 2014, p. 515). Deep learning requires “new ways of thinking and transformative solutions (Chen & Lin, 2014, p. 523). Another pair of researchers from China present a broad overview of the various societal, business and administrative applications of Big Data, including a detailed account and definitions of the processes and tools accompanying Big Data analytics. The American counterparts of these Chinese researchers are of the same opinion when it comes to “think about the core principles and concepts that underline the techniques, and also the systematic thinking” (Provost and Fawcett, 2013, p. 58). De Mauro, Greco, and Grimaldi (2016), similarly to Provost and Fawcett (2013) draw attention to the urgent necessity to train new types of specialists to work with such data. As early as 2012, Davenport and Patil (2012), as cited in Mauro et al (2016), envisioned hybrid specialists able to manage both technological knowledge and academic research. Similarly, Provost and Fawcett (2013) mention the efforts of “academic institutions scrambling to put together programs to train data scientists” (p. 51). Further, Asomoah, Sharda, Zadeh & Kalgotra (2017) share a specific plan on the design and delivery of a big data analytics course. At the same time, librarians working with data acknowledge the shortcomings in the profession, since librarians “are practitioners first and generally do not view usability as a primary job responsibility, usually lack the depth of research skills needed to carry out a fully valid” data-based research (Emanuel, 2013, p. 207).
Borgman (2015) devotes an entire book to data and scholarly research and goes beyond the already well-established facts regarding the importance of Big Data, the implications of Big Data and the technical, societal, and educational impact and complications posed by Big Data. Borgman elucidates the importance of knowledge infrastructure and the necessity to understand the importance and complexity of building such infrastructure, in order to be able to take advantage of Big Data. In a similar fashion, a team of Chinese scholars draws attention to the complexity of data mining and Big Data and the necessity to approach the issue in an organized fashion (Wu, Xhu, Wu, Ding, 2014).
Bruns (2013) shifts the conversation from the “macro” architecture of Big Data, as focused by Borgman (2015) and Wu et al (2014) and ponders over the influx and unprecedented opportunities for humanities in academia with the advent of Big Data. Does the seemingly ubiquitous omnipresence of Big Data mean for humanities a “railroading” into “scientificity”? How will research and publishing change with the advent of Big Data across academic disciplines?
Reyes (2015) shares her “skinny” approach to Big Data in education. She presents a comprehensive structure for educational institutions to shift “traditional” analytics to “learner-centered” analytics (p. 75) and identifies the participants in the Big Data process in the organization. The model is applicable for library use.
Being a new and unchartered territory, Big Data and Big Data analytics can pose ethical issues. Willis (2013) focusses on Big Data application in education, namely the ethical questions for higher education administrators and the expectations of Big Data analytics to predict students’ success. Daries, Reich, Waldo, Young, and Whittinghill (2014) discuss rather similar issues regarding the balance between data and student privacy regulations. The privacy issues accompanying data are also discussed by Tene and Polonetsky, (2013).
Privacy issues are habitually connected to security and surveillance issues. Andrejevic and Gates (2014) point out in a decision making “generated by data mining, the focus is not on particular individuals but on aggregate outcomes” (p. 195). Van Dijck (2014) goes into further details regarding the perils posed by metadata and data to the society, in particular to the privacy of citizens. Bail (2014) addresses the same issue regarding the impact of Big Data on societal issues, but underlines the leading roles of cultural sociologists and their theories for the correct application of Big Data.
Library organizations have been traditional proponents of core democratic values such as protection of privacy and elucidation of related ethical questions (Miltenoff & Hauptman, 2005). In recent books about Big Data and libraries, ethical issues are important part of the discussion (Weiss, 2018). Library blogs also discuss these issues (Harper & Oltmann, 2017). An academic library’s role is to educate its patrons about those values. Sugimoto et al (2012) reflect on the need for discussion about Big Data in Library and Information Science. They clearly draw attention to the library “tradition of organizing, managing, retrieving, collecting, describing, and preserving information” (p.1) as well as library and information science being “a historically interdisciplinary and collaborative field, absorbing the knowledge of multiple domains and bringing the tools, techniques, and theories” (p. 1). Sugimoto et al (2012) sought a wide discussion among the library profession regarding the implications of Big Data on the profession, no differently from the activities in other fields (e.g., Wixom, Ariyachandra, Douglas, Goul, Gupta, Iyer, Kulkami, Mooney, Phillips-Wren, Turetken, 2014). A current Andrew Mellon Foundation grant for Visualizing Digital Scholarship in Libraries seeks an opportunity to view “both macro and micro perspectives, multi-user collaboration and real-time data interaction, and a limitless number of visualization possibilities – critical capabilities for rapidly understanding today’s large data sets (Hwangbo, 2014).
The importance of the library with its traditional roles, as described by Sugimoto et al (2012) may continue, considering the Big Data platform proposed by Wu, Wu, Khabsa, Williams, Chen, Huang, Tuarob, Choudhury, Ororbia, Mitra, & Giles (2014). Such platforms will continue to emerge and be improved, with librarians as the ultimate drivers of such platforms and as the mediators between the patrons and the data generated by such platforms.
Every library needs to find its place in the large organization and in society in regard to this very new and very powerful phenomenon called Big Data. Libraries might not have the trained staff to become a leader in the process of organizing and building the complex mechanism of this new knowledge architecture, but librarians must educate and train themselves to be worthy participants in this new establishment.
Method
The study will be cleared by the SCSU IRB.
The survey will collect responses from library population and it readiness to use and use of Big Data. Send survey URL to (academic?) libraries around the world.
Data will be processed through SPSS. Open ended results will be processed manually. The preliminary research design presupposes a mixed method approach.
The study will include the use of closed-ended survey response questions and open-ended questions. The first part of the study (close ended, quantitative questions) will be completed online through online survey. Participants will be asked to complete the survey using a link they receive through e-mail.
Mixed methods research was defined by Johnson and Onwuegbuzie (2004) as “the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts, or language into a single study” (Johnson & Onwuegbuzie, 2004 , p. 17). Quantitative and qualitative methods can be combined, if used to complement each other because the methods can measure different aspects of the research questions (Sale, Lohfeld, & Brazil, 2002).
Sampling design
Online survey of 10-15 question, with 3-5 demographic and the rest regarding the use of tools.
1-2 open-ended questions at the end of the survey to probe for follow-up mixed method approach (an opportunity for qualitative study)
data analysis techniques: survey results will be exported to SPSS and analyzed accordingly. The final survey design will determine the appropriate statistical approach.
Project Schedule
Complete literature review and identify areas of interest – two months
Prepare and test instrument (survey) – month
IRB and other details – month
Generate a list of potential libraries to distribute survey – month
Contact libraries. Follow up and contact again, if necessary (low turnaround) – month
Collect, analyze data – two months
Write out data findings – month
Complete manuscript – month
Proofreading and other details – month
Significance of the work
While it has been widely acknowledged that Big Data (and its handling) is changing higher education (https://blog.stcloudstate.edu/ims?s=big+data) as well as academic libraries (https://blog.stcloudstate.edu/ims/2016/03/29/analytics-in-education/), it remains nebulous how Big Data is handled in the academic library and, respectively, how it is related to the handling of Big Data on campus. Moreover, the visualization of Big Data between units on campus remains in progress, along with any policymaking based on the analysis of such data (hence the need for comprehensive visualization).
This research will aim to gain an understanding on: a. how librarians are handling Big Data; b. how are they relating their Big Data output to the campus output of Big Data and c. how librarians in particular and campus administration in general are tuning their practices based on the analysis.
Based on the survey returns (if there is a statistically significant return), this research might consider juxtaposing the practices from academic libraries, to practices from special libraries (especially corporate libraries), public and school libraries.
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p. 501 – is political science “softer” than the other soft social sciences?
thus… political science “may never live up to its lofty ambition of scientific explanation and prediction. Indeed, like other social sciences, it can be no more than a ‘ science in formation’ permanently seeking to surmount obstacles to objectivity.”
p. 502 disciplinary parochialism
the fetishes of pure observation, raw experience, unambiguous rationality, and one-way causality were formative influences in the genesis of the social sciences. the ‘unfortunate positivism” of such impulses, along with the illusion of a value-free science, converged to produce a behavioral revolution in the interwar period Behaviorism was then followed through an epistemological twist, by boldly optimistic leaps to an “end of ideology” and ultimately to a claimed “end of history” itself.
p. 503
early positivism was openly underpinned by an European condescension toward Asians’ “ignorance and prejudice.” Behind similar depictions lay a comprehensive Eurocentric social and political philosophy.
this is illustrated its view of China through the grand narrative of modernization.
p. 504
Robert McNamara famously reiterated that if World War I was a chemist’s war and Word War II a physicist’s, Vietnam “might well have to be considered the social scientists’ war.”
Although China nominally remains a communist state, it has doubtlessly changed color without a color revolution.
p. 505
In the fixed disciplinary eye, “China” is to specific to produce anything generalizable beyond descriptive and self-containing narratives. The area studies approach, in contrast to disciplinary approaches, is all about cultural, historical, and ethnographic specificities.
If first-hand information contradicts theoretical conclusions, redress is sought only at the former end (my note – ha ha ha, such an elegant but scathing criticism of [Western] academia).
The catch [is] that Chinese otherness is in essence not a matter of cultural difference (hence limitations of criticizing Eurocentrism and Orientalism) and does not merely reproduce itself by inertia.
Given a long omitted self-critical rethinking of the discipline’s parochial base, calling for cross-fertilizing alone would be fruitless or even lead only to a one-way colonization of seemingly particularistic histories by an illusive universal science.
p. 506
political culture, once a key concept of political science’s hope for unified theorization, has turned out to be no answer
Long after its heyday, modernization theory – now with its new face of globalization – remains a primary signifier and legitimating benchmark. To those, who use it to gauge developments since 1945, private property and liberal democracy are permanent, unquestioned norms that are to be globally homogenized.
Moreover, since modernity is assumed to be a liberal capitalists condition, the revolutionary nationalism of an oppressed people remaking itself into a new historical subject noncompliant with capitalism cannot be modernizational.
p. 507
Political scientists and historical sociologists… saw the communist in power as formidable modernizers, but distinguished the Maoist model from the Stalinist in economic management and campaign politics.
Their analyses showed how organic connections between top-down mobilization and bottom-up participation cultivated in an active citizenry and high intensity politics. My note: I disagree here with the author, since such statement can be arbitrary from a historical point of view; indeed, for a short period of time, such “organic connection” can produce positive results, but once calcitrated (as it is in China for the past 6-7 decades), it turns stagnant.
p. 510
the state’s altered support base is essentially a matter of class power, involving both adaptive cultivation of new economic elites and iron-fist approaches to protest and dissent. By the same weight of historical logic, the party’s internal decay, loss of its founding ideological vision and commitment, and collusion with capital will do more than any outside force ever could do to destroy the regime.
That the Party stays in power is not primarily because the country’s economy continues to grow, but is more attributable to a residual social reliance on its credentials and organizational capacities accumulated in earlier revolutionary and socialist struggles. This historical promise has so far worked to the extent that cracks within the leadership are more or less held in check, resentment against local wrongs are insulated from central intentions, and social policies in one way or another respond to common outcries, consultative deliberations, and pressure groups.
p. 511
The word “madness” has indeed been freely employed to describe nations and societies judged inept at modern reason, as found in contemporary academic publications on epi- sodes of the PRC history. My note: I agree with this – the deconstructionalists: (Jaques Derrida, Tzvetan Todorov) linguistically prove the inability of Western cultures to understand and explain other cultures. In this case, Lin Chun is right; just because western political scientist cannot comprehend foreign complex societal problems and/or juxtaposing them to their own “schemes,” prompts the same western researchers to announce them as “mad.”
p. 513aa
This is the best and worst of times for the globalization of knowledge. In one scenario, an eventual completion of the political science parameters can now seal both knowledge, sophisticatedly canalized, and ideology, universally uncontested – even if the two are never separable in the foundation of political science. In another scenario, causes and effects no longer rule out atypical polities, but the differences are presented as culturally incompatible. In either case, the trick remains to let anormalies make the norms validate preexist- ing disciplinary sanctions.
p. 514
Overcoming outmoded rigidities will nurture a robust scholarship committed to universally resonant theories.