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Record W4200364493 · doi:10.1177/25148486211066109

Invasive Plant Relations in a Global Pandemic: Caring for a “Problematic Pesto” <sup/>

2021· article· en· W4200364493 on OpenAlexaffabout
Gabrielle Doiron

Bibliographic record

VenueEnvironment and Planning E Nature and Space · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTemporalitiesIndigenousTransformative learningEnvironmental justiceSociologyEcologyPrecarityEconomic JusticeColonialismEnvironmental ethicsPolitical scienceLawBiologyGender studies

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.291
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2021
Admission routes2
Has abstractyes

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