Hurricane Katrina Hair: Rereading Nineteenth-Century Commemorative Hair Forms and Fragments Through the “Mourning Portraits” of Loren Schwerd
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".