Sustainable Urban Foragings in the Canadian Metropolis: Rummaging through Rita Wong’s Forage and Nicholas Dickner’s Nikolski
Bibliographic record
Abstract
Foraging and dumpster diving are two activities now associated with a kind of environmentally-conscious social activism engaged in by people wanting to live sustainably through maintaining a close connection with their local environment; the former is generally associated with nature, the latter with the urban. Wong's poetry collection and Dickner's novel, both featuring those who scavenge within their urban environment, enable a connection to be made between these activities; to label dumpster diving 'urban foraging' is to make clear the way the dumpster diver helps us to interrogate the urban/nature binary. This article uses Michel de Certeau's 'Walking in the City', in particular, to think through Wong and Dickner's figurations of the two Canadian metropolises: Vancouver and Montreal. These cities become places of subversive urban foraging; the garbage becomes transformed through renewed visibility, while in turn the urban space is re-made, potentially, as a place of sustainable possibility.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".