Bibliographic record
Abstract
This book offers a historical analysis of landfill sites in New York City, Greater Toronto, and Greater Tel Aviv, and uses them as case studies to emphasize the international and global scale of issues concerning waste disposal and park redevelopments. New York, Toronto, and Tel Aviv are currently redeveloping giant landfills into parks to much fanfare. The park redevelopments may be seen as an attempt to erase or assuage the decades of problematic waste-disposal policy that led to the creation of such large landfills. Booster rhetoric underscores this point, such as promoting how the parks will be a “green lung” for the city. This book contextualizes these redevelopments by offering a historical analysis, providing a greater understanding of the past, current, and future potential issues. It goes on to analyze the language and media coverage surrounding former waste sites becoming park redevelopments, including how cities use art to promote their image and gain cultural relevance. By engaging with both the works of waste historians and literature on waste and discard studies, the book provides theoretical models for analyzing the role of power in municipal systems, as well as human and ecological impacts on waste. It concludes with an analysis of the features necessary for landfill parks to be successful. This book will be useful for scholars, researchers, and academics studying waste studies, the environment, cities, and sustainable development, as well as for policymakers and environmental/eco artists.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.018 | 0.037 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".