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Record W3049471227 · doi:10.33915/etd.3797

Listening for the Cosmic Other: Postcolonial Approaches to Music in the Space Age

2019· dissertation· en· W3049471227 on OpenAlexfundno aff
Paige Zalman

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSpace Science and Extraterrestrial Life
Canadian institutionsnot available
FundersConnaught FundUniversity of TorontoGovernment of Canada
KeywordsExtraterrestrial lifeSpace (punctuation)MusicalIndigenousAestheticsHistoryVisual artsArtMedia studiesSociologyArt historyAstrobiologyPhilosophyPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.603
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.089
GPT teacher head0.292
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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