Listening for the Cosmic Other: Postcolonial Approaches to Music in the Space Age
Bibliographic record
Abstract
As government programs such as NASA and SETI seek signs of intelligent life in space and privately-funded programs such as SpaceX finalize plans to colonize Mars in the coming decades, representations of space and extraterrestrial life in American culture have become increasingly relevant. Focusing on Jóhann Jóhannsson’s musical score for Denis Villeneuve’s science-fiction film Arrival (2016), Terry Riley’s Sun Rings (2002) for string quartet, chorus, and recorded space sounds, and former International Space Station Commander Chris Hadfield’s “Songs about Space” Spotify playlist, my research problematizes the ways in which composers, musicians, and even astronauts depict alterity through music and reinforce colonial narratives about outer space. Drawing upon the work of postcolonial theorists and musicologists such as Charles Forsdick (2003), Olivia A. Bloechl (2008), Ania Loomba (2015), and others, this thesis argues that musical depictions of extraterrestrials and space exploration, more generally, reveal the potential for discrimination, misrepresentation, and abuses of power to emerge from space colonization. But, back on Earth, this study also suggests that such fraught theoretical relationships between human colonists and extraterrestrials echo the real-life suffering of colonized indigenous groups and the necessity of decolonization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.063 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".