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Record W3080931801 · doi:10.38055/fs020101

Hurricane Katrina Hair: Rereading Nineteenth-Century Commemorative Hair Forms and Fragments Through the “Mourning Portraits” of Loren Schwerd

2019· article· en· W3080931801 on OpenAlexaffvenue
E. G. Berry

Bibliographic record

VenueFashion Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPortraitImmediacyNarrativePower (physics)HistoryHurricane katrinaArt historyArtSociologyLiteratureAnthropologyPhilosophyNatural disasterGeography

Abstract

fetched live from OpenAlex

This article examines sculptural portraits by artist Loren Schwerd. Fashioned from hairpieces discovered in the 2005 wreckage of Hurricane Katrina, they are memorials to the African American victims and evacuees of the storm. Their title, Mourning Portrait, recalls nineteenth-century traditions of mourning and commemorative hairwork in which the locks of living and dead loved ones were manipulated into intricate fashions and home décor. They also incorporate African American hairstyling techniques to interpret the flood-ravaged homes of local residents. Thus, on one hand, they take inspiration from Victorian hairwork traditions, which channeled the talismanic power of hair fragments to evoke absent bodies and memory. On the other hand, they expand and politicize the meanings of commemorative hair forms and fragments toward evoking collective histories, memories, and larger social issues, bringing new urgency and immediacy to fashion-related material cultures of mourning. Exploring the interlinked narratives of Schwerd’s “mourning portraits” and Victorian hairwork, this article uses cultural theory, material culture studies, archival research, fashion theory, and African American studies to broaden critical insights into state-sanctioned racial and class-based violence, and modes of resistance that take shape through aesthetic and representational forms.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.540

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.285
Teacher spread0.236 · 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 routes2
Has abstractyes

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