Invasive Plant Relations in a Global Pandemic: Caring for a “Problematic Pesto” <sup/>
Bibliographic record
Abstract
In Spring 2020, amidst a COVID-19 state of emergency, the City of Toronto's Parks & Urban Forestry department posted signs in the city's remaining Black Oak Savannahs to announce the cancellation of the yearly 'prescribed burn' practice, citing fears it would exacerbate pandemic conditions. With this activity and other nature management events on hold, many invasive plants continued to establish and proliferate. This paper confronts dominant attitudes in invasion ecology with Indigenous epistemologies and ideas of transformative justice, asking what can be learned from building a relationship with a much-maligned invasive plant like garlic mustard. Written in isolation as the plant began to flower in the Black Oak savannahs and beyond, this paper situates the plant's abundance and gifts within pandemic-related 'cancelled care' and 'cultivation activism' as a means of exploring human-nature relations in the settler-colonial city. It also asks what transformative lessons garlic mustard can offer about precarity, non-linear temporalities, contamination, multispecies entanglements, and the impacts of colonial property regimes on possible relations. Highlighting the entanglements of historical and ongoing violences with invasion ecology, this paper presents 'caring for invasives' as a path toward more liveable futures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".