Category: Uncategorized

Task 9: Network Assignment Using Golden Record Curation Quiz Data

I used the dataset from our Golden Record Curation Quiz and uploaded the .json file into Palladio to visualize how participants’ musical choices connect to one another. In this network, each node represents either a participant or a piece of music, while each edge shows a shared selection. What I find special is that the resulting visualization forms a web of interconnected tastes, showing clusters of participants linked by similar aesthetic or cultural preferences.

When I examined the Palladio visualization (Figure 1), several noticeable patterns appeared based on the number of edges connecting participants and tracks.

Figure 1

At the center of the network, compositions such as The Well-Tempered Clavier, Brandenburg Concerto (First Movement), and Fifth Symphony (First Movement) formed a dense core cluster. This area connected many participants who shared a preference for classical and orchestral works, reflecting an attraction to structured and historically significant pieces.

Moving outward, a smaller grouping emerged around Bagpipes (Azerbaijan), Tsuru No Sugomori (Crane’s Nest), and Night Chant. This cluster represented participants who selected pieces rooted in folk, Indigenous, or world music traditions, suggesting curiosity toward cultural diversity and traditional soundscapes.

In contrast, more isolated nodes, such as Rite of Spring (Sacrificial Dance) and Johnny B. Goode, extended outward from the main web, highlighting individual or experimental preferences that did not strongly overlap with others. These outliers illustrated how certain musical tastes remain unique within a collective dataset.

Interestingly, a few participants who selected pieces from both classical and world music categories acted as bridges between clusters. Their choices visually linked otherwise separate groups, symbolizing how some individuals naturally cross stylistic and cultural boundaries through music.

In network analysis terms, this visualization maps nodes (participants or tracks), edges (shared choices), and degree centrality, which measures how many connections each node has. As explained in our Module 9 lesson materials, “users like us are, after all, also nodes in the network, and our interests, behaviours, and activities change the relations within the network constantly”.

Each participant’s musical selections increased the weight of certain connections, strengthening ties between particular tracks and individuals. The result was a network organized not by geography or identity but by patterns of shared affinity: a digital echo of how algorithmic systems group people based on their online behaviour and preferences.

While the graph effectively visualizes who is connected to whom through shared musical preferences, it cannot tell us why these choices were made. For example:

• Two participants might both select the same song (e.g. Beethoven’s Fifth Symphony), but one may appreciate its historical influence while another associates it with a personal memory.
• The graph also cannot capture non-selections or the null choices representing tracks we chose not to include. These absences, though invisible in the visualization, hold deep cultural and personal meaning.

This limitation reminds us that what is left out of a dataset, the unchosen, the unheard, is as significant as what appears within it.

The visualization reflects the algorithmic logic underpinning many digital systems today. Like social media recommendation engines, it groups individuals through measurable similarities. Although the image appears neutral, it subtly reinforces assumptions about identity and belonging:

By clustering people based on quantifiable data, it simplifies complex cultural and emotional experiences into neat categories.

It privileges connection over context, what can be measured over what can be felt, mirroring the “attention economy” described in our lesson materials, where algorithms use patterns of behaviour to predict and influence human choices.

Politically, this raises concerns about bias and representation. The visualization lacks metadata such as participants’ backgrounds, emotional motivations, or interpretive reasons. What we see, then, is a partial portrait: a constructed network that omits the richness of human subjectivity, echoing larger debates around data visualization as a tool of both revelation and erasure.

Working on this visualization reminded me how easily technology can flatten personal experiences into abstract data. When I made my selections in the Golden Record quiz, each track carried emotional meaning like memories of travel, family, or teaching moments. Yet, in the Palladio graph, those emotions were reduced to points and lines. This realization made me reflect on my own digital identity: how algorithms categorize me based on patterns rather than stories, and how individuality can resist such systematization. This experience deepened my awareness of the tension between connection and reduction that defines our networked lives.

References

Code.org. (2017, June 13). The internet: How search works [Video]. YouTube. https://youtu.be/LVV_93mBfSU

Cornec, O. (2015). WikiGalaxy: Explore Wikipedia in 3D. Experiments with Google. https://experiments.withgoogle.com/wikigalaxy

Willingham, K. (n.d.). The golden record. Poetry Foundation. https://www.poetryfoundation.org/poems/156957/the-golden-record

Task 4: Manual Scripts and Potato Printing

I normally do most of my academic and creative work on digital devices like my Macbook or my iPad, so writing this reflection by hand felt unusual but also refreshing. On a laptop, I can rearrange sentences, erase mistakes instantly, and polish my drafts quickly. Handwriting, by contrast, slowed me down and made me think more carefully before I put words on the page. It reminded me of the historical shift from scroll to codex: just as the codex allowed readers to flip back and forth more deliberately (From Scroll to Codex, 2025), handwriting pushed me to write with greater intention than typing usually does.

When I made mistakes, I reached for correction tape instead of simply pressing “delete.” This act took extra time and interrupted the flow of writing. It left small traces on the page that reminded me of pre-mechanized writing, when revisions were physically inscribed into the medium itself. The readings on mechanization note how Gutenberg’s press brought efficiency and uniformity (Mechanization: Before and After, 2025), but I realised that mechanical neatness often hides the pauses, hesitations, and revisions that handwriting makes visible.

I also noticed how handwriting reflects character and emotion. The speed of my strokes, the slight unevenness of letters, and even the spots where I pressed harder with my pen all revealed my mood while writing. This felt far more personal than typed text which is uniform regardless of who produces it. Bolter’s (2001) idea of remediation came to mind here: new technologies like typing may be faster and tidier, but handwriting carries a tactile authenticity that typing cannot replicate.

For me, the biggest difference between handwriting and mechanized writing lies in how each relates to time. Handwriting feels embodied, personal, and expressive, while mechanized forms (printed or digital form) are built around speed and mass distribution. Innis’s (2007) point about the printing press accelerating knowledge production made me reflect on how much I value typing for productivity. Still, this task reminded me that handwriting invites a slower and more emotional kind of reflection, one rooted in patience and presence, like how my favourite book The Little Prince reminded me of.

References

Bolter, J. D. (2001). Writing space: Computers, hypertext, and the remediation of print (2nd ed). Lawrence Erlbaum Associates.

From Scroll to Codex: ETEC_V 540 64A 2025W1 Text Technologies: The Changing Spaces of Reading and Writing. (2025). University of British Columbia.

Innis, H. (2007). Empire and communications. Dundurn Press.

Mechanization: Before and After: ETEC_V 540 64A 2025W1 Text Technologies: The Changing Spaces of Reading and Writing. (2025). University of British Columbia.

Economies of Writing -or- Writing About Writing: ETEC_V 540 64A 2025W1 Text Technologies: The Changing Spaces of Reading and Writing. (2025). University of British Columbia.

For task 3, I am asked to speak an unscripted, 5 minute long story into a voice-to-text app. Hence, I will first print my unscripted story here as reference.

My unscripted, 5-min story: Okay, so let me think where to start oh yeah last week I had this kind of funny kind of annoying experience on my way to school so normally I drive but that day my car just wouldn’t start the completely dead and of course it was the one morning I was already running late, so I thought fine. I’ll just take the bus. It’s been ages since I Lasted that and you know how when you’re in a rush every little thing feels bigger the bus took fewer forever to come I swear like 20 minutes and when finally that it was completely packed, I’m standing there holding onto the pole half asleep and there’s this little kid just staring at me the whole right like full of staring at first it was killed you know kids are curious, but then it was like 10 minutes in and he’s still looking at me and I’m like do I have something on my face anyway? so the bus was crawling along then I was watching the time and I realise I was going to be super late so I texted my colleague to cover my first few minutes and then right when I was typing the bus slammed on the bricks my phone went flying out of my hand like literally slid three seats forward everyone looked at me and a kid left so I shuffled forward grabbed my phone both sorry to the people glaring at me and then I noticed I’ve accidentally send my colleague half a text like it just sets I will be and then nothing so she called me and of course it’s super loud on the bus and I was yelling into my phone. I’m on the bus and I’ll be late everyone still looking at me at that point. I was just like great let’s make this the most awkward ride ever so finally I got off walk the rest of the way and when I arrived at school, my students were ready sitting there waiting and the first thing one of them said was teacher you look tired and I just saw yeah things you have no idea but honestly looking back it’s kind of funny now because it’s been so long since I wrote the bus and I kind of forgot that Whole fight of being stuck with strangers the small embarrassing moments and also just weird little stories that come from it so yeah, maybe my car breaking down wasn’t that bad it gave me something to love about later.

After analysing the written script I got, here are my comments:

1. How does the text deviate from conventions of written English?

The written text deviates from the conventions of written English in several ways. Most noticeably, it contains very long run-on sentences without clear punctuation or paragraph breaks (which I have already foreseen). Spoken pauses such as “oh yeah,” “anyway,” or “so” appear, but because they are not punctuated in the transcript, the story reads as one breathless stretch of text. Moreover, there are many oral fillers. These oral fillers like “kind of,” “you know,” and “so” are common in speech but look redundant in a formal written work. There are also inconsistencies in tense and grammar, such as “the completely dead” instead of “completely dead,” or “It’s been ages since I Lasted that,” which does not make sense in written English.

2. What is “wrong” in the text? What is “right”?

To me, “wrong” in the text refers to the mistakes found in a “written text,” while “right” in the text refers to “the authenticity of an oral speech.”

What seems “wrong” in the text are these issues of grammar, transcription, and structure. First off, the lack of punctuation makes the text hard to follow. Also, the transcription errors such as “bricks” instead of “brakes” or “a kid left” instead of “a kid laughed” distort the original meaning when it was said orally. At the same time, much of the story is “right” in terms of oral speech.  The conversational tone, the use of fillers, and those personal comments create authenticity and show the speaker’s tone and voice. There are many emotional words such as “funny,” “annoying,” and “awkward” which give the story colour and a hint of personal touch.

3. What are the most common “mistakes” in the text and why are they “mistakes”?

The most common mistakes in the written text are those run-on sentences, speech-to-text mishearings, redundant fillers, and inconsistent grammar. These are considered mistakes from the perspective of written English because readers expect to see punctuation, accurate grammar, and precise word choice in written text. However, when we speak, we do not read out those punctuations, and those AI-tools might also mistakenly transcribe the original spoken words as some other words (due to noise of the surroundings and mispronunciation). These mistakes show the difference between how speech is produced spontaneously and how writing is carefully constructed.

4. What if you had “scripted” the story? What difference might that have made?

If the story had been scripted, the result would have been very different, as a scripted version would include proper punctuation, well-formed sentences, and corrected grammar. Mis-transcriptions like “bricks” would not appear. The story would be smoother and easier to read, but it would also lose the authentic rhythm of natural conversation. The hesitations, fillers, and small mistakes that make it sound spontaneous would disappear, meaning that the whole text would be more polished but less genuine.

5. In what ways does oral storytelling differ from written storytelling?

After doing this task, I think oral storytelling and written storytelling differ in several ways. Oral storytelling allows for spontaneity, digressions, fillers, and on-the-spot corrections. It relies on tone, pauses, and body language to give meaning. On the other hand, a written one requires structure, coherence, and conciseness. It is because readers cannot rely on non-verbal cues (like body language). Errors that pass unnoticed in speech become highly visible in writing. Oral stories flow in unbroken chains of thought, while written stories must be broken into paragraphs and sentences, with punctuations to guide readers.