I just read the TEI article. I agree with @Hayley Madl that it is a bit difficult to understand without knowing what TEI is in the first place. I also looked at this website to familiarize myself with it more: https://tei-c.org/ Basically it seems to be a language that specifically describes digital text, which is useful for defining characteristics that aren’t defined by other languages. Like you can define the type of material a manuscript is written on: <material function=“support”>Parchment</material>. It uses xml, which I am familiar with but it also resembles html. I have no experience with this so I am not sure when/how it is used but it sounds like something a digital historian really needs to know! (edited)
For the TEI technical activity I encoded a graphic and its position within the (imaginary) text page numbers. From the inside out – the graphic url itself is inside the figure tag, which is a container for the graphic. The figure tag and graphic are inside the pb tag, which is the beginning and end page on which the graphic appears. These are inside the body tag. There is a front tag and back tag before and after the body section where I put some text. These would be like the title page and end page. Then I put a little bit in the header.
<TEI xmlns=“http://www.tei-c.org/ns/1.0”>
<teiHeader>
<fileDesc>
<titleStmt>
<title>Lincoln School</title>
<respStmt>
<resp>Photo taken by</resp>
<name>Larry Roeder</name>
</respStmt>
</titleStmt>
</teiHeader>
<text>
<front>
<docTitle>
<titlePart type=“main”>An Image of a one-room Schoolhouse</titlePart>
</docTitle>
</front>
<body>
<pb n=“1”/>
<figure>
<graphic url=“Lincoln1.png”/>
</figure>
<pb n=“2”/>
</body>
<back>
<div type=“notes”>Lincoln, Virginia</div>
</back>
</text>
</TEI>
The article on crowdsourcing by Causer and Wallace was interesting especially because it gave so much real data on some important problems with crowdsourcing. A few years ago I found out about zooniverse and I signed up to identify images in space. It was fun for a while and then I got tired of it. I was definitely in the lightweight category. I wasn’t part of a community or really invested in the project, it was just a whim. On the other hand, I worked for the Smithsonian as a contractor a couple of years ago. I got to know people and to care about project I worked on. After it was over, I wanted to continue as a volunteer because I was invested. So, even though this is not a crowdsourcing project, I feel I am in the heavyweight category because I want to continue working with it. This article gives some good insight on how to build a community of volunteers who are invested instead of random, short-lived volunteers. It gives results of their research on what worked well and didn’t work well in setting up a good crowdsourcing project including how the volunteers were found, demographics and, importantly, how they tried to retain interest and find out what rewards people hoped to get from doing the work. Another important thing to note was the time it takes to do this. As the article writers pointed out for their specific project, by the time they got the crowdsourcing running fairly smoothly, it was time for the project to be over.
If you click on the xml view for the Causer article you can see the text encoding which is in TEI / xml. It doesn’t include any style information so all you are seeing is the text and the tags that are separating the different elements like headings, quotes, text, images, etc.
