Some “F” words for the environmental humanities: feralities, feminisms, futurities
Bibliographic record
Abstract
In Toronto, the city where I live, there is an extraordinary place called, variously, Tommy Thompson Park (TTP), the Leslie Street Spit, the Outer East Harbour Headland, or simply “the Spit.” This piece of land, stretching five kilometers into Lake Ontario, was (and continues to be) created out of the detritus of Toronto’s development. Starting in the 1950s, when it was intended to create a breakwater to support increased shipping on the Great Lakes, the Spit has received tens of thousands of tons of waste. Assembled from everything from building teardowns to subway construction to shipping channel dredgeate, the Spit is a rubbly archive of the city’s history. As Watt-Meyer shows, visitors can, with a bit of digging, locate particular urban remains at specific points on the Spit and know that they are walking on the grave of, for example, the Toronto Board and Trade Building (demolished 1958). Moreover, as Schopf and Foster demonstrate, the Spit tells a larger story about urban development and environmental justice. Deposits from 1960s slum clearances contain large numbers of personal artifacts, indicating that “full houses with belongings still inside were demolished, compacted, and then dumped” (1092). Subsequent deposits from the 1980s are “much more uniform and organised” (1095): by this period, “there was considerable planning for the afterlife of the rubble” (1103), a rationalized folding of waste, as it were, into the aesthetic and political matrices of capitalism.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.064 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".