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Record W4353029428 · doi:10.3917/rhsho.217.0367

13. Playing for a public: French Manouche commemorations of Roma genocide

2023· article· fr· W4353029428 on OpenAlexaff
Siv B. Lie

Bibliographic record

VenueRevue d’Histoire de la Shoah · 2023
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsMusée de la Civilisation
FundersAgence Nationale de la Recherche
KeywordsSilenceScholarshipThe HolocaustNazismGenocideAmbivalenceRacismMusicalSociologyAestheticsHistoryArtHumanitiesGender studiesPolitical scienceLiteraturePsychoanalysisPsychologyLawGerman

Abstract

fetched live from OpenAlex

Scholarship about French Manouches often emphasizes prohibitions on speaking about past traumas and the dead, but there are exceptions to this silence. This article explores instances in which Manouche musicians are publicly vocal about death and trauma in prior generations. Their outspokenness is particularly evident in recent public-facing musical works that commemorate the Nazi genocide of Roma people. The producers of these works self-consciously endeavor to make themselves legible within contemporary frameworks of Holocaust commemoration. These efforts should be taken as evidence of multiple and ambivalent attitudes toward death and trauma that exist among Manouche individuals and communities. In breaking the silence, the work of these artists signals generational shifts in attitudes about how to pay respect to the dead. Close attention to cultural productions such as music offers a nuanced understanding of Manouche memory practices and may thus contribute to struggles against a long legacy of anti-Roma racism in France.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.068
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.012
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.039
GPT teacher head0.245
Teacher spread0.206 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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