Uncomfortable quilts: textile-based artivism in response to Bangladeshi garment factory disasters
Bibliographic record
Abstract
Recently, two deadly garment factory disasters in Dhaka, Bangladesh – the 2012 Tazreen Fashions factory fire (117 killed; over 200 injured) and the 2013 collapse of Rana Plaza, an eight story complex including garment factories (1,135 killed, over 2,500 injured) – inspired a series of artworks addressing globalisation, gendered labour exploitation, memorialisation, and the power of empathy. This essay explores the work of four visual artists: Robin Berson, Taslima Akhter, Reetu Sattar, and Dilara Begum Jolly. Each engages in physically and/or emotionally challenging creative processes, including enactments of repetitive garment labour, weaving the names and faces of deceased workers into textiles, and displaying personal effects such as family photographs salvaged from the ruins of the destroyed factories. In this way, the artists call attention to the human cost of so-called ‘fast fashion’ and agitate for moral responsibility in the face of these disasters. More universally, this essay offers examples of how visual art can expose the causal dimensions of structural violence and socio-economic power imbalances while also memorialising, expressing solidarity, and aiding with community healing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".