WIkipedia’s reputation has improved from its inception, a site with the radical idea that non-experts should be able to contribute to knowledge on the world wide web. Not long ago, I would not consider Wikipedia a valid resource, but while I would not use it as a main source, I have found it is often a jumping off point for research. Understanding how Wikipedia entries is important in evaluating the value of an article and its accuracy. Given that chat bots also use it as one of their large language models, the ability to review entries and how they develop is useful.

Using the Digital Humanities page as an example, the “Talk” and “View history” sections provide insight into the development of the page and its contents. “View history” allows the viewer to see how the site has changed, as well as a side by side comparison of what each change did to the site. It also identifies the editor, linking to their user page and frequently a description of their expertise. 

In the screenshot above, examples include a 27 November 2022 edit for unsourced material being added; a 3 June 2022 addition on Analysis of macroscopic trends in cultural change was added, then changed slightly.; and on 5 January 2022 entries reflecting the rendition of the unsourced work with comment, and the subsequent removal of it again. The 27 November 2022 change is made by ElKevbo, the third highest contributor to the site, and/or his biography, works in the digital humanities and higher learning.

Adding to this are comments in the “Talk” section, a discussion of what content should be added. I particularly liked the following post, discussing areas to improve their understanding and that of other users:

The section also discusses some of the editing decisions and collaboration, such as an early crowdsourcing event at THATcamp (The Humanities and Technology Camp) in Australia making several edits. The first large batch of edits were made by the in the field thinking and learning about the digital humanities.

Understanding the source of the information, and that Wikipedia editors are trying to maintain community standards, lends credibility to the entries. AI crowdsources information through the text it was trained on, including Wikipedia. While it synthesizes information rapidly, and by using large, internet based data files, it is still only as good as the crowd it is using. Its inability to show sources makes it another useful starting point, but not necessarily an ending point. I queried Chat GPT about its use of Wikipedia, with this response:

I don’t have direct access to the internet or specific databases like Wikipedia in real-time. Instead, I’ve been trained on a diverse range of internet text available up until my last update in 2022, which includes information from sources like Wikipedia. My responses are generated based on patterns and information learned from that data.

If you ask me a question about a topic, I’ll generate a response based on the knowledge I’ve acquired during my training. So, while I can’t access Wikipedia directly, I’ve learned from its content and can provide information based on what I’ve previously read and absorbed.

Wikipedia, a world of words askew,
A dance of data, a linguistic brew.
No capital chains to bind its grace,
In ee cummings’ style, it finds its place.

A symphony of facts in a tangled rhyme,
Hyperlinks weaving through the sands of time.
No rigid structure, just free-flowing streams,
In Wikipedia’s garden, knowledge dreams.

Lowercase whispers tell tales untold,
As wikis unfold, in pixels bold.
Syntax surrenders, conventions flee,
In this textual playground, wild and free.

Beneath the letters, a universe blooms,
A kaleidoscope of knowledge looms.
No need for order, no need for rule,
Wikipedia dances in the vastness, cool.

A symphony of voices, a chorus untamed,
In cummings’ spirit, where no word’s framed.
Wikipedia, a poem of pixels and links,
In the language of chaos, information syncs.

wiki leaks words, a
cacophony, a
melange of info
sprawling unchained un
bridled in its verve un
stop-pable unwavering it grows

words dance in links
a tap dance of thoughts
meandering through time space
no capitals or spaces
just the pulse of info
in a torrential race

oh, wikipedia!
encyclopedic cacophony
where knowledge meets chaos
in a glorious blend
of facts and conjecture
yet a haven for seekers
in a digital splendor un
matched and profound

The poems above illustrate both the questionable and good about AI crowdsourcing. The Chat GPT prompt was “write a poem in the style of ee cummings.”The first try has the syntax and rhythm of Cummings, but it was not until the second try that it picked up the trait Cummings is known for, the lack of capitalization. AI did not recognize initially, although the Wikipedia entry on Cummings describes it, with several examples of poems.

The challenge of crowdsourcing, through a Wikipedia or Wikipedia of the future or AI, remains the expertise and the biases of the crowd. Lockett engagement with her students at Spelman as being motivated by realizing how many gaps in information existed about their college and those like it, and Black women in particular¹. Crowdsourcing brings a myriad of experiences, expertise and interests to the world in a very shareable way, however moving into the future, identifying the gaps and blind spots will be critical to their overall effectiveness as a tools.

1. Lockett, Alexandria. “Why Do I Have Authority to Edit the Page? The Politics of User Agency and Participation on Wikipedia.” In Wikipedia @ 20: Stories of an Incomplete Revolution. Edited by Joseph Reagle and Jackie Koerner (MIT Press,
   2020), https://doi.org/10.7551/mitpress/12366.003.0019Links to an external site.., p 213. ↩︎