Bibliographic record
Abstract
This essay emerges from conversations between an anthropologist and a performance artist from the industrial city of Taranto, in southern Italy—known today to be one of Europe’s most polluted cities due to the continent’s largest and most hazardous steel factory. By focusing on artist Isabella Mongelli’s photographic and theatrical work conducted upon returning to her hometown after years lived abroad, this essay locates the expression of a specific affect coined by Taranto sound artist Alessandra Eramo: la tristezza siderurgica , the sorrow of steel. Tristezza siderurgica yields an ethnographic understanding of the perceptions and representations of homes that have become homely, uncanny, estranged because toxic or otherwise inhospitable. The article shows that the naming of affects contributes to forging specific, perhaps even untranslatable, emotional landscapes. What does it mean to be emotionally laced by the poison of steel? While contemporary texts on the experience of environmental toxicity tend to waver between the material and semiotic/metaphorical as distinct poles, this essay proposes that we braid these oft-polarized terms through the work of ethnography. Through this dialogic process between aesthetics and anthropology, we encounter the ways in which artists have sought to understand ecological crises through a mobilization of the senses, which are crucial for understanding toxicity as it vacillates between visibility and invisibility.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".