Industrial ruination, community, and place landscapes and legacies of urban decline
Bibliographic record
Abstract
Abandoned factories, shipyards, warehouses, and refineries are features of many industrialized cities around the world. But despite their state of decline, these derelict sites remain vitally connected with the urban landscapes that surround them. In this enlightening new book, Alice Mah explores the experiences of urban decline and post-industrial change in three different community contexts: Niagara Falls, Canada/USA; Newcastle-upon-Tyne, UK; and Ivanovo, Russia. Employing a unique methodological approach that combines ethnographic, spatial, and documentary methods, Mah draws on international comparisons of the landscapes and legacies of industrial ruination over the past forty years. Through this, she foregrounds the complex challenges of living with prolonged uncertainty and deprivation amidst socioeconomic change. This rich comparative study makes an essential contribution to far-reaching debates about the decline of manufacturing, regeneration, and identity, and will have important implications for urban theory and policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".