Category: Week 3

Pennsylvania’s Anthracite Coal Region

Pennsylvania’s Anthracite Coal Region

There are a plethora of occupations that are dependent upon the formation of the land in a certain location.  The development and design of a landscape has a lasting impact on the means of production and labor that create an effective economy. In central Pennsylvania, the anthracite coal mining region began in roughly 1792 with the founding of the Lehigh Coal Mine Company by Josiah White and Erskine Hazard. This was the first commercially operated coal mine. The anthracite region would see new mines developing throughout prominently six counties: Luzerne, Lackawanna, Carbon, Northumberland, Columbia, and Schuylkill. Thus geographically, there are northern, middle, and southern anthracite fields.

As anthracite mining began spreading rapidly throughout central Pennsylvania, many problems arose surrounding working conditions, town life, pay, etc. It was common for miners to have major and minor accidents within and outside of the mines. There were many perilous activities a miner had to perform.

The coal companies began hiring boys to work in the mines as early as ages six to eight. They would pick rock, slate, and refuse out of the coal in the breaker; an arduous and dangerous task for a young boy.[1] There were usually contract miners, laborers, foremen, and superintendents that worked per colliery. There was a large inequality of wages between laborers and miners that caused a further divide between ethnic enclaves. The Welsh immigrants who were skilled miners who usually became the foremen and superintendents of a colliery. While Irish immigrants were “distrusted” by the coal owners and other miners. This was partly because of the Molly Maguire movement, who were accused of murder, arson, and other crimes throughout the mining districts.

Nevertheless, the dangers and the perils of a coal miner were extreme. The mines could be very difficult to mine in without proper lightning, protection, ventilation, or other essential equipment. Usually due to poor ventilation, mine fires would trap miners in the coal mines and the only means of escape was the main shaft, which at times would be destroyed by flames.[2] Other mining accidents included accidents due to poor quality of the colliery with columns falling on people, and commonly floods, which would occur when a geological depression in the mine filled with water and created a flood in the mine.[3] If a miner did not die from an accident in the coal mine, it was common to die from “miner’s lung” or “black lung” from the sulfur toxins in the mines. In order to provide for their families and contribute to the economic prosperity of their town, miners would sacrifice their well-being and risk death.[4]

In order to preserve the heritage and culture of the coal mining industry, while paying homage to the coal miners and their families, some anthracite communities choose to represent coal miners through monuments. These representations are sometimes specific to a certain town’s coal history, however, many represent the greater Pennsylvania anthracite region.

By critiquing the two out-facing public mediums, newspapers and monuments, I will investigate how Pennsylvania anthracite coal communities choose to represent their coal heritage.

[1]  Janet MacGaffey, Coal Dust on Your Feet: The Rise, Decline, and Restoration of an Anthracite Mining Town (Lewisburg, PA: Bucknell University Press, 2013), 23.

[2] Ibid., 25.

[3] Ibid., 25.

[4] The few descriptions of different types of accidents in the coal mines is not an exhaustive list. There were many other perils and dangers depending on the skill level of a miner or laborer and the quality of the colliery and its owners.

Week 3 – Justin

Representation in Cinema is becoming a prominent issue with the film industry with the growing focus on identity. One of the areas the film industry seems to have trouble representing is the LGBTQ+ community. While Hollywood seems to have a harder time getting representation right, Independent film picks up the slack. The questions that shaped this project are: Why does Independent cinema do a better job at representing LGBTQ+ people than Hollywood cinema which reaches a larger audience than Independent cinema? Is the Hollywood film industry playing it safe with their general audience? How are films that represent LGBTQ+ people produced (Hollywood and Independently) and does representation affect how the film is received financially and qualitatively? Is the representation positive or negative?

According to GLAAD’s 2016 Studio Responsibility Index out of the 126 films that were released by the 7 major Hollywood Production companies only 17.5% actually depicted LGBT characters and according to their 2017 Studio Responsibility Index only 18.4% depicted LGBT characters. While there is an increase in the amount of representation, there’s clearly room for improvement. LGBT adults make up  4.1% of the US population and (as of 2014) about 3% of the Canadian population. It goes without being said that 4.1% or 3% of an entire country’s population is a substantial number and these people aren’t being represented as well as they could in mainstream Hollywood cinema.

In order to answer these questions, data was collected from films that are both produced by Hollywood and Independently. This data includes production companies, budgets, profits, screenplays, and reception of films released in North America from the past 40 years. This data will be used to give people an insight to what representation currently looks like in both film industries and what some of the reasons behind it are. With this information people will be able to discuss representation and come to a conclusion that can be different from my own or similar. The point is to get people talking about the direction of representation in cinema because that’s the only way solutions can be found.


Week 3 Recap: Data is Everywhere, and Everything is Data

I’ve spent the past week mulling over the concept of data visualization — honing in more on the first word, data. What exactly is data, anyway? What do we consider to be data in the context of digital scholarship? I don’t want to go deeply into the rabbit hole here, but as I’ve delved more into the world of digital scholarship, it’s become evident that so much of what we do is based on some form of data — be that numerical data, textual data, geospatial data, audiovisual data, etc. So even though this week was data visualization week, data viz is actually a common theme throughout our program. Last week we experimented with Voyant as a means of visualizing textual data, and in Week 4 we’ll be looking at timelines, maps, and networks — all of which require data in one form or another. Data really is everywhere in digital scholarship! And with that, here’s how Week 3 unfolded.

On Monday, Jill Hallam-Miller and Ben Hoover joined us for a session on data literacy. Before we took a deep dive into learning how to make our own data visualizations, it was important to think about what makes a visualization good, bad, or somewhere in between. We drew our on visualizations based off a dataset of first year students’ hometowns, deconstructed a number of visualizations, came up with our own criteria for evaluating visualizations, and talked about data cleaning. I’m confident that our students will be looking at any data visualizations they encounter in the future with a critical eye!

On Tuesday, we were joined by our colleague Ken Flerlage, for the first of a two-part workshop on Tableau. Among the many data visualization tools currently available, Tableau sits at the juncture of tools that are robust in terms of what they can do both presentation wise and analysis wise. Although there are a lot of intricacies in figuring out exactly how to visualize a dataset, the underlying premise of Tableau is that you create a worksheet for each graph, and then put them all together into a dashboard, which can be stylized with text and images. Our students picked up the process pretty quickly, and Thursday we tested our newfound skills by working with a dataset from the #MakeoverMonday project. Each week, the facilitators of #MakeoverMonday post a link to a chart and its data, and invite anyone to redesign the chart. This week’s dataset was from the Tate Collection; you can check out our students’ vizzes here: Minglu; Rennie; Justin.

We ended our week with a bit of fun and took a walk over to the College of Engineering to visit the Maker-E, Bucknell’s electronics maker space. The space has a lot of tools for designing and working with electronics components, 3D printers, a vinyl sticker cutter, and more. We spent most of our time playing with the littleBits, which are sort of like electronic Legos. There are LED lights, speakers, a mini keyboard, and many other parts that you can connect together to make little gadgets. Tyler even figured out how to connect his phone to the littleBits speakers so he could play music (and the tiny speakers sounded pretty good!).

It’s hard to believe we’re already heading into Week 4 of the program, aka the halfway point. The students are deep into their research and continue to make great progress! We’ve got another full week lined up, including a trip to Bryn Mawr College to meet up with other students who are in programs similar to ours.

Week 3

Internet use in China has been on the rise for a long time, but in the past few years we witnessed an extremely significant jump in Internet penetration rate. By June 2016, there were 710 million Internet users in China and Internet penetration was at 51.7%. This is a leap from 39.9% in June 2012. (CNNIC, 38th Statistical Report)

As a result, the e-commerce sector in China is booming. Alibaba(B2B), T-Mall(B2C) and Taobao(C2C), all part of the Alibaba Group founded by Jack Ma in 1999, takes center stage and individually they dominate the markets of each of their platforms. (Statista, 2016) Alibaba Group’s main rival, Tencent, which founded WeChat, an instant messaging and social media platform, is also put in the spotlight as its expanded functions make it an all-in-one mobile application that many now heavily depend on. Online services are also on the rise, among which online meal ordering and online transportation services have proven to be particularly successful.

National Internet penetration rate may have increased steadily, but does each individual province in China benefit from the increased Internet use to the same extent? Also, what are the social implications that come together with the rise of Internet use, particularly in the context of China? These are some questions we will seek to explore.



China Internet Network Information Center (July 2016) Statistical Report on Internet Development in China (38th report), Retrieved from

Statista (2016) Alibaba Group, Retrieved from file:///C:/Users/User/Downloads/study_id23850_alibaba-group-statista-dossier.pdf

Week 3 – Rennie Heza

Jarome Iginla for Joe Nieuwendyk. Wayne Gretzky for Jimmy Carson and others. Trades like these permanently altered the NHL landscape in the late 20th century, and today, hockey professionals would unanimously agree that one team faired better than the other in both of these trades.

Subban for Weber, Hall for Larsson, and Johansen for Jones. These noteworthy NHL trades have all taken place in the past 18 months. Though immediate results can be cited, the “winners” of these trades are hard to name, as all six of the players listed above are still playing at a high level. Until recently, only time told which teams made out better in personnel exchanges, but the ongoing development of advanced hockey analytics is changing this.

Made famous by Billy Beane and the improbable success of his Oakland Athletics, number-supported personnel decisions are changing the way sports franchises are run. Though only recently introduced to the hockey world, respected voices in hockey have already voice their support for hockey’s newest asset. At an end of season press conference in 2016 Bill Peters, head coach of the NHL’s Carolina Hurricanes and former coach of Team Canada’s Under 18 team said about the use of analytics “It’s all about finding ways to be more successful. I embrace it” (Smith). Though analytics is now respected in the NHL, very few predictive tools have been created.

Hockey analysts such as Emmanuel Perry have created single-value metrics that determine a player’s worth, while others have focused on offensive and defensive efficiency metrics. Though both of these tools can be helpful in assessing the potential outcome of personnel decisions, I believe creating a model to accurately predict team performance given personnel decisions is the next step in hockey analytics. This project is intended to allow fans and hockey professionals alike the ability to assess future team success given front office decisions.

Creating a successful model will have a multitude of uses, including but not limited to helping guide personnel decisions, providing comparative evaluations of individual player contributions, and gambling applications.

Will the Montreal Canadians regret losing PK Subban? Was acquiring Adam Larsson really worth losing Taylor Hall? We may find out sooner than expected which of these teams faired better, and which teams might be haunted by these trades for years to come.

Smith, M. (2016, April 12). Francis, Peters Digest 2015-16 Season. Retrieved June 10, 2017, from