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Record W4320729175 · doi:10.1177/13591835221135404

Tourists of their own past: Aural palimpsests from the Mao era

2023· article· en· W4320729175 on OpenAlexfundno aff
Shelley Zhang

Bibliographic record

VenueJournal of Material Culture · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHong Kong and Taiwan Politics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaSchool of Arts and Sciences, University of Pennsylvania
KeywordsPoliticsNegotiationAestheticsEthnographyTourismMeaning (existential)SociologyChinaGovernment (linguistics)HistoryCultural revolutionVisual artsMedia studiesAnthropologySocial scienceLawArtPolitical sciencePsychologyLinguisticsArchaeology

Abstract

fetched live from OpenAlex

This article explores how individuals in contemporary China use songs to both express and protect their memories of the Cultural Revolution and Mao era. As individuals who experienced the Cultural Revolution find ways to voice their recollections of the past, they casually listen to and perform songs from the Mao era, pursue domestic tourism, and engage with other material culture from that time. Their actions index individuals’ complicated nostalgias and continual negotiations with their political presents. Drawing from ethnographic fieldwork in the Hunan province, this article analyzes song objects as ‘aural palimpsests’ that allow individuals to gesture towards their political presents without criticizing the government or articulating traumatic memories. Aural palimpsests are performed and heard in architectural spaces that shape how music from different eras and genres become layered atop one another to create new social meaning in a contemporary China that is still grappling with its recent history.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.291
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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