Breathing new futures in polluted environments (Taranto, Italy)
Bibliographic record
Abstract
Abstract This paper analyses the ways that young people create new futures in Taranto, Southern Italy, a city hosting one of the largest and most polluting steel factories in Europe. It draws on ethnographic fieldwork in Taranto and uses storytelling to understand how young people – a minority of residents aged between 24 and 35 years – shape futures in industrially polluted environments. The study weaves together geographic and anthropological scholarship about futures in (post‐)industrial cities, conceptualisations of breathing as well as lived experiences in highly polluted areas. Through mobilising the notion of breathing, we highlight the embodied, entangled, and emotional dimensions of the young people's everyday practices and develop our concept of “breathing new futures.” We argue that both pollution and the envisioning of a new future become visible in everything the study's participants do; the ways they promote environmental awareness, take care of animals, or seek to foster children's education. By focusing on generational differences, the study expands on recent scholarship analysing environmental pollution in relation to intersectional identities such as race, ethnicity, and gender, and sheds light on the activities of young people to imagine and live new futures in polluted environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".