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Record W7039619954

Music After The Cultural Revolution: Transnational Precarity For China's One-Child Generation

2022· article· en· W7039619954 on OpenAlexaboutno aff

Bibliographic record

VenueScholarlyCommons (University of Pennsylvania) · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsPrecarityScholarshipRedressChinaEthnographyInterculturalismMusicalEthnomusicology
DOInot available

Abstract

fetched live from OpenAlex

This dissertation studies the intergenerational effects of the Chinese Cultural Revolution and the transnational careers of Chinese musicians born and raised during the one-child policy. Bridging scholarship in ethnomusicology and musicology with work in cultural anthropology, East Asian studies, Asian American studies, and literature studies, I examine the palpable memories and traumas from the Cultural Revolution and show that they continually frame individual and collective engagements with music in the People’s Republic of China and the Chinese diaspora. At the core of this research are extensive multi-sited ethnography conducted in the PRC, Taiwan, Canada, and the United States, and archival sources. Using these interdisciplinary methodologies, I address Chinese music-making practices in relation to personal and familial desires, and national transformations. I argue that although some Chinese musicians have achieved the highest levels of institutional success through conservatory training and international performances, they are continually motivated by anxieties of socio-economic precarity and desires for redress from parents who lost musical ambitions during the Cultural Revolution. As a result, Chinese musicians use what I term “strategic citizenship” to create transnational opportunities and seek stable futures for themselves while navigating neoliberal systems that impact their educational pathways and possibilities for new residency in countries such as Canada and the United States.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.998

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 studies0.0030.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.070
GPT teacher head0.216
Teacher spread0.146 · 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.

Study designNot applicable
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
Published2022
Admission routes1
Has abstractyes

Explore more

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