Posts Tagged ‘quantitative method’

Critical Infrastructure Studies & Digital Humanities

Critical Infrastructure Studies & Digital Humanities

Alan Liu, Urszula Pawlicka-Deger, and James Smithies, Editors

Deadline for 500-word abstracts: December 15, 2021

For more info:
https://dhdebates.gc.cuny.edu/page/cfp-critical-infrastructure-studies-digital-humanities

Part of the Debates in the Digital Humanities Series A book series from the University of Minnesota Press Matthew K. Gold and Lauren F. Klein, Series Editors

Defintion
Critical infrastructure studies has emerged as a framework for linking thought on the complex relations between society and its material structures across fields such as science and technology studies, design, ethnography, media infrastructure studies, feminist theory, critical race and ethnicity studies, postcolonial studies, environmental studies, animal studies, literary studies, the creative arts, and others (see the CIstudies.org Bibliography )

CI Studies Bibliography

Debates in the Digital Humanities 2019

https://dhdebates.gc.cuny.edu/projects/debates-in-the-digital-humanities-2019

teaching quantitative methods:
https://dhdebates.gc.cuny.edu/read/untitled-f2acf72c-a469-49d8-be35-67f9ac1e3a60/section/620caf9f-08a8-485e-a496-51400296ebcd#ch19

Problem 1: Programming Is Not an End in Itself

An informal consensus seems to have emerged that if students in the humanities are going to make use of quantitative methods, they should probably first learn to program. Introductions to this dimension of the field are organized around programming languages: The Programming Historian is built around an introduction to Python; Matthew Jockers’s Text Analysis with R is at its heart a tutorial in the R language; Taylor Arnold and Lauren Tilton’s Humanities Data in R begins with chapters on the language; Folgert Karsdorp’s Python Programming for the Humanities is a course in the language with examples from stylometry and information retrieval.[11] “On the basis of programming,” writes Moretti in “Literature, Measured,” a recent retrospective on the work of his Literary Lab, “much more becomes possible”

programming competence is not equivalent to competence in analytical methods. It might allow students to prepare data for some future analysis and to produce visual, tabular, numerical, or even interactive summaries; Humanities Data in R gives a fuller survey of the possibilities of exploratory data analysis than the other texts.[15] Yet students who have focused on programming will have to rely on their intuition when it comes to interpreting exploratory results. Intuition gives only a weak basis for arguing about whether apparent trends, groupings, or principles of variation are supported by the data. 

From Humanities to Scholarship: Librarians, Labor, and the Digital

Bobby L. Smiley

https://dhdebates.gc.cuny.edu/read/untitled-f2acf72c-a469-49d8-be35-67f9ac1e3a60/section/bf082d0f-e26b-4293-a7f6-a1ffdc10ba39#ch35

First hired as a “digital humanities librarian,” I saw my title changed within less than a year to “digital scholarship librarian,” with a subject specialty later appended (American History). Some three-plus years later at a different institution, I now find myself a digital-less “religion and theology librarian.” At the same time, in this position, my experience and expertise in digital humanities (or “digital scholarship”) are assumed, and any associated duties are already baked into the job description itself.

Jonathan Senchyne has written about the need to reimagine library and information science graduate education and develop its capacity to recognize, accommodate, and help train future library-based digital humanists in both computational research methods and discipline-focused humanities content (368–76). However, less attention has been paid to tracking where these digital humanities and digital scholarship librarians come from, the consequences and opportunities that arise from sourcing librarians from multiple professional and educational stations, and the more ontological issues associated with the nature of their labor—that is, what is understood as work for the digital humanist in the library and what librarians could be doing.

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More on digital humanities in this blog
https://blog.stcloudstate.edu/ims?s=Digital+humanities

gamification online learning

Gunawan, F. (2018). GAMIFICATION ANALYSIS AND IMPLEMENTATION IN ONLINE LEARNING. ICIC Express Letters, 12(12), 1195–1204.
https://www.academia.edu/39858461/GAMIFICATION_ANALYSIS_AND_IMPLEMENTATION_IN_ONLINE_LEARNING?auto=download
Khan [14] has introduced the eight-dimensional elearning framework, a detailed self assessment instrument for institutions to evaluate the readiness and the opportunity of their e-learning classes to grow.
institutional, management, technological, pedagogical, ethical, interface design, resource support, and evaluation. Institutional refers to the administrative and academic part of the system. Management refers to the quality control, budget, and scheduling. Technological refers to the infrastructure, hardware, and software. Pedagogical refers to analysis, organization and learning strategies. Ethical refers to ethical, legal, and social and political influences. Interface design refers to the user interface, accessibility, and design content. Resource support refers to career services, journals, and online forums. Finally, the evaluation refers to the assessment of learners and educators.
gamification – definition
Modern gamification term was first introduced by
Nick Pelling in 2002 [15]. Gamification is a concept that implements the game components
into the non-game contents such as education, marketing, administration, or even software
engineering [16]. These components include points, badges, leaderboards, and quests.
Each of them serves the purpose to increase the level of user engagement in the learning
process.
three components of engagement: cognitive, behavioral, and emotional [19].
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more on gamification and online learning in this IMS blog
https://blog.stcloudstate.edu/ims?s=gamification+online+learning

no Millennials Gen Z Gen X

Can We Please Stop Talking About Generations as if They Are a Thing?

Millennials are not all narcissists and boomers are not inherently selfish. The research on generations is flawed.
DAVID COSTANZA
APRIL 13, 2018 9:00 AM

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SIVA VAIDHYANATHAN, 2008. https://www.chronicle.com/article/Generational-Myth/32491 Generational Myth
My note: Siva raised this issue from a sociologist point of view as soon as in 2008. Before him, Prensky’s “digitally natives” ideas was already criticized.
Howe and Strauss; Millennials books contributed to the overgeneralizations. https://en.wikipedia.org/wiki/Strauss%E2%80%93Howe_generational_theory
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We spend a lot of time debating the characteristics of generations—are baby boomers really selfish and entitledare millennials really narcissists, and the latest, has the next generation (whatever it is going to be called) already been ruined by cellphones? Many academics—and many consultants—argue that generations are distinct and that organizations, educators, and even parents need to accommodate them. These classifications are often met with resistance from those they supposedly represent, as most people dislike being represented by overgeneralizations, and these disputes only fuel the debate around this contentious topic.

In short, the science shows that generations are not a thing.

It is important to be clear what not a thing means. It does not mean that people today are the same as people 80 years ago or that anything else is static. Times change and so do people. However, the idea that distinct generations capture and represent these changes is unsupported.

What is a generation? Those who promote the concept define it as a group of people who are roughly the same age and who were influenced by a set of significant events. These experiences supposedly create commonalities, making those in the group more similar to each other and more different from other groups now and from groups of the same age in the past.

In line with the definition, there is a commonly held perception that people growing up around the same time and in the same place must have some sort of universally shared set of experiences and characteristics. It helps that the idea of generations intuitively makes sense. But the science does not support it. In fact, most of the research findings showing distinct generations are explained by other causes, have serious scientific flaws, or both.

For example, millennials score lower on job satisfaction than Gen Xers, but are millennials really a less satisfied generation? Early in their careers, Xers were also less satisfied than baby boomers.

Numerous booksarticles, and pundits have claimed that millennials are much more narcissistic than young people in the past.
on average, millennials are no more narcissistic now than Xers or boomers were when they were in their 20s, and one study has even found they might be less so than generations past. While millennials today may be more narcissistic than Xers or boomers are today, that is because young people are pretty narcissistic regardless of when they are young. This too is an age effect.

Final example. Research shows that millennials joining the Army now show more pride in their service than boomers or Xers did when they joined 20-plus years ago. Is this a generational effect? Nope. Everyone in the military now shows more pride on average than 20 years ago because of 9/11. The terrorist attack increased military pride across the board. This is known as a period effect and it doesn’t have anything to do with generations.

Another problem—identifying true generational effects is methodologically very hard. The only way to do it would be to collect data from multiple longitudinal panels. Individuals in the first panel would be measured at the start of the study and then in subsequent years with new panels added every year thereafter, allowing assessment of whether people were changing because they were getting older (age effects), because of what was happening around them (period effects), or because of their generation (cohort effects). Unfortunately, such data sets pretty much do not exist. Thus, we’re never really able to determine why a change occurred.

According to one national-culture model, people from the United States are, on average, relatively individualistic, indulgent, and uncomfortable with hierarchical order.
My note: RIchard Nisbett sides with Hofstede and Minkov: https://blog.stcloudstate.edu/ims/2016/06/14/cultural-differences/
Conversely, people from China are generally group-oriented, restrained, and comfortable with hierarchy. However, these countries are so large and diverse that they each have millions of individuals who are more similar to the “averages” of the other country than to their own.

Given these design and data issues, it is not surprising that researchers have tried a variety of different statistical techniques to massage (aka torture) the data in an attempt to find generational differences. Studies showing generational differences have used statistical techniques like analysis of variance (ANOVA) and cross-temporal meta-analysis (CTMA), neither of which is capable of actually attributing the differences to generations.

The statistical challenge derives from the problem we have already raised—generations (i.e., cohorts) are defined by age and period. As such, mathematically separating age, period, and cohort effects is very difficult because they are inherently confounded with one another. Their linear dependency creates what is known as an identification problem, and unless one has access to multiple longitudinal panels like I described above, it is impossible to statistically isolate the unique effect of any one factor.

First, relying on flawed generational science leads to poor advice and bad decisions. An analogy: Women live longer than men, on average. Why? They engage in fewer risky behaviors, take better care of themselves, and have two X chromosomes, giving them backups in case of mutations. But if you are a man and you go to the doctor and ask how to live longer, she doesn’t tell you, “Be a woman.” She says eat better, exercise, and don’t do stupid stuff. Knowing the why guides the recommendation.

Now imagine you are a manager trying to retain your supposedly job-hopping, commitment-averse millennial employees and you know that Xers and boomers are less likely to leave their jobs. If you are that manager, you wouldn’t tell your millennial employees to “be a boomer” or “grow older” (nor would you decide to hire boomers or Xers rather than millennials—remember that individuals vary within populations). Instead, you should focus on addressing benefits, work conditions, and other factors that are reasons for leaving.

Second, this focus on generational distinctions wastes resources. Take the millennials-as-commitment-averse-job-hoppers stereotype. Based on this belief, consultants sell businesses on how to recruit and retain this mercurial generation. But are all (or even most) millennials job-hopping commitment avoiders? Survey research shows that millennials and Xers at the same point in their careers are equally likely to stay with their current employer for five or more years (22 percent v. 21.8 percent). It makes no sense for organizations to spend time and money changing HR policies when employees are just as likely to stick around today as they were 15 years ago.

Third, generations perpetuate stereotyping. Ask millennials if they are narcissistic job-hoppers and most of them will rightly be offended. Treat boomers like materialistic achievement seekers and see how it affects their work quality and commitment. We finally are starting to recognize that those within any specific group of people are varied individuals, and we should remember those same principles in this context too. We are (mostly) past it being acceptable to stereotype and discriminate against women, minorities, and the disabled. Why is it OK to do so to millennials or boomers?

The solutions are fairly straightforward, albeit challenging, to implement. To start, we need to focus on the why when talking about whether groups of people differ. The reasons why any generation should be different have only been generally discussed, and the theoretical mechanism that supposedly creates generations has not been fully fleshed out.

Next, we need to quit using these nonsensical generations labels, because they don’t mean anything. The start and end years are somewhat arbitrary anyway. The original conceptualization of social generations started with a biological generational interval of about 20 years, which historians, sociologists and demographers (for one example, see Strauss and Howe, 1991) then retrofitted with various significant historical events that defined the period.

The problem with this is twofold. First, such events do not occur in nice, neat 20-year intervals. Second, not everyone agrees on what the key events were for each generation, so the start and end dates also move around depending on what people think they were. One review found that start and end dates for boomers, Xers, and millennials varied by as many as nine years, and often four to five, depending on the study and the researcher. As with the statistical problem, how can distinct generations be a thing if simply defining when they start and when they end varies so much from study to study?

In the end, the core scientific problem is that the pop press, consultants, and even some academics who are committed to generations don’t focus on the whys. They have a vested interest in selling the whats (Generation Me has reportedly sold more than 115,000 copies, and Google “generations consultants” and see how many firms are dedicated to promulgating these distinctions), but without the science behind them, any prescriptions are worthless or even harmful

David Costanza is an associate professor of organizational sciences at George Washington University and a senior consortium fellow for the U.S. Army Research Institute. He researches, teaches, and consults in the areas of generations, leadership, culture, and organizational performance.

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more on the topic in this IMS blog
https://blog.stcloudstate.edu/ims?s=millennials