Using Golden Record Curation Quiz Data
The Palladio visualization of our class’s Golden Record selections appears to offer an objective representation of shared musical preferences, yet a closer analysis reveals how deeply political and partial such visualizations are. As the New London Group (1996) argues, all meaning-making is shaped by multiliteracies- our diverse cultural and semiotic resources- rather than by neutral or universal understandings. Palladio’s multimodal network map constructs a narrative that clusters participants around similar tracks, implying shared cultural alignments or values. However, these clusters tell only a partial story about why participants, including myself, selected the music we did.
In my own experience completing the Golden Record activity, some choices reflected familiarity rather than genuine preference. I gravitated toward tracks with recognizable structures or instrumentation, not necessarily because I valued them more, but because they felt accessible within my existing cultural background. Yet the visualization flattens these decisions into identical data points. As Haraway (1988) warns, visual technologies often create a “view from nowhere,” obscuring the subjective contexts that shape knowledge and representation. Palladio suggests that participants who chose the same tracks share meaningful similarities, even though their intentions, experiences, and cultural literacies may differ widely.
Equally significant are the “null” choices- the tracks participants did not select. These absences are socio-culturally meaningful, yet the visualization renders them invisible. A participant may avoid a track because of linguistic unfamiliarity, emotional discomfort, perceived cultural appropriation, or simple time constraints. These reasons carry political weight, reflecting power dynamics around whose music feels approachable or intelligible. However, Palladio treats non-selection as informationally empty. Simpson (2014) reminds us that cultural texts cannot be extracted from their contexts without losing essential meaning. By flattening cross-cultural and ceremonial pieces into comparable units of “data,” the visualization replicates the epistemic violence embedded in the original Voyager project, which appropriated global cultural artifacts under the guise of representing a universal humanity.
This raises important implications for my future professional practice. As educators, we increasingly navigate data dashboards, analytics, and algorithmic tools that categorize learners based on reductive indicators. The Golden Record visualization demonstrates how easily such tools can produce misleading groupings and reinforce dominant ideologies about culture, taste, or ability. Understanding these limitations encourages me to remain critical of data-driven narratives and to prioritize context, dialogue, and student voice over superficial patterns in visualized data.
The visualization also assumes that tracks are comparable and that selection is a straightforward indication of preference. This is not always the case. The Golden Record collection spans continents, genres, languages, and centuries, yet each track is treated as equal. This flattening has political implications: it risks erasing context, power histories, and cultural differences. For instance, Indigenous songs, ritual chants, and ceremonial pieces appear side-by-side with Bach and Beethoven, despite having distinct cultural meanings and purposes. When participants’ choices are used to cluster them, it can unintentionally reproduce hierarchies- e.g., Western tracks appearing more “popular,” not because they are intrinsically preferred, but because they are more familiar to a Western-educated audience.
Therefore, while the visualization shows connections, it does not show intention. It identifies communities, but it does not explain why those communities appear. It highlights shared selections, but it cannot capture the social, cultural, and emotional forces behind them. Most importantly, it cannot reflect the reasons behind the silent data- the choices that were never made. Understanding these limitations is crucial for interpreting not only musical preferences but also the broader politics of data visualization: what it shows, what it hides, and what it makes us believe about ourselves and each other.
Ultimately, the visualization functions less as a map of musical preference and more as a prompt for critical reflection on data, culture, and power. It pushes us to question what stories visualizations tell, what they erase, and how our own choices- visible and invisible- are shaped by broader ideological forces that must be acknowledged in transformative educational practice.
References and Disclaimer:
Haraway, D. J. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599. https://doi.org/10.2307/3178066
New London Group. (1996). A pedagogy of multiliteracies: Designing social futures. Harvard Educational Review, 66(1), 60–92. https://doi.org/10.17763/haer.66.1.17370n67v22j160u
Simpson, L. B. (2014). Land as pedagogy: Nishnaabeg intelligence and rebellious transformation. Decolonization: Indigeneity, Education & Society, 3(3), 1–25.
Steen, J., & Schraff, C. (2015-). Palladio: A tool for visualizing complex historical data [Software]. Stanford University. https://hdlab.stanford.edu/palladio-app/