Garbage mountains: the use, redevelopment, and artistic representation of New York City's Fresh Kills, Greater Toronto's Keele Valley, and Tel Aviv's Hiriya landfills
Bibliographic record
Abstract
Garbage landfills are at the heart of debates over sustainable urban development. Landfills are the cheapest waste-disposal method, but have specific environmental problems and are a common target for citizen activism such as environmental justice and Not In My Backyard (NIMBY) protests. As a means of covering up the scars at recently closed landfills, it has been common for cities to redevelop landfills into parks. The ongoing redevelopment projects at New York City's Fresh Kills, Greater Toronto's Keele Valley, and Greater Tel Aviv's Hiriya landfills are uniquely ambitious and large-scale projects, because these three landfills were among the largest in the world at the time each of them closed around the turn of the twenty-first century. These three landfill-park redevelopments are positive projects, but there are more complexities involved than one would find discussed in booster rhetoric such as government press releases, local newspaper descriptions, and even museum exhibitions. The construction of Freshkills Park, North Maple Regional Park, and Ariel Sharon Park does little to address the ongoing waste-disposal policy concerns of New York, Toronto, and Tel Aviv; therefore, the redevelopments have more significance as “symbols” of a poor past policy being replaced by a “progressive” policy for a better future than as actual waste-disposal policies. Artists and landscape architects have created works based on the theme of parkland as a fresh start for these landfills, in gallery and museum exhibitions such as Hiriya in the Museum at the Tel Aviv Museum of Art in 2000 and artwork created by acclaimed environmental artist Mierle Laderman Ukeles for Fresh Kills.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".