Tattered Cloth Tells More: Women's Work and Museum Representation
Bibliographic record
Abstract
The past two decades posed some challenges for the museum world. Questions about the production of meaning, museum relationships with community groups, and the politics of representation in exhibitions, occupy both museum practitioners and scholars. These questions are further related to the general issues that are at the forefront of contemporary society, which include problems of social inclusion, cultural diversity and social equity (Sandell, 2002; 2007). Most of the discussion has been framed around racial, ethnic and cultural communities and their access to and participation in museum programming. Gender relations and feminist issues have been largely overlooked (Conlan and Levin, 2010: 308). This study considers the representation of women’s work in museums. In particular, I examine portrayals of “culture” and “work” in women’s textile production. Museum literature has documented the subordination (or absence) of women and their work in exhibitions and the hegemonic, patriarchal approach within which they were represented (Porter, 1996; Levin, 2010).\nUsing an ethnographic case study of a museum dedicated to textile collection, I suggest seeing this museum as a potential challenge to mainstream museums’ traditional approach and silence on the women’s work that has created most textiles on display. I examine the meanings that are produced in relation to the textiles, the organization and dissemination of these meanings through exhibitions and the ways in which the public (visitors and members) responds to these exhibitions. In order to explore these questions, Hall’s communication model (1993) was applied to trace the process of encoding and decoding meanings at the museum. My approach to meaning production is realized through observations of the museum’s committee meetings. The second stage is the circulation of meanings in exhibitions. I examine this through an analysis of exhibitions’ texts and docent tours. Decoding these meanings is realized through surveys of museum members and visitors together with short interviews. My findings suggest that initially, the museum offered some oppositional elements in exhibiting practices. However, a shift occurred towards a dominant, hegemonic view of museum work and re-effacing of women’s work with the departure of the founders.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".