Settler colonialism in vegetal worlds: exploring progress and resilience at the margins of the Anthropocene
Bibliographic record
Abstract
This paper explores the intersections of vegetal ontologies and contemporary practices of settler colonialism. The rising popularity of the concept of the Anthropocene fails to give account to nonhuman encounters that shape contemporary sensibilities. As such, this paper explores the role of vegetal being in settler colonial practices to disrupt the efficacy of anthropocentric representations of the world. In concepts like ‘invasive species,’ ‘blight’ and ‘resilience,’ settler colonial metaphors construct the vegetal world alongside the practices of colonial expansion and capitalist globalization. To recover the occlusion of the vegetal world from purview, three cases will be explored to connect the role of automation (as mechanical processes and unconscious action) with the failures and inherent limitations of the settler project. This exploration begins with an examination of Fordlândia's failed efforts to develop scientific agriculture in the Amazon in the 1920s through settler fantasies of progress. This is followed by an examination of the concept of urban blight in the racialized reconfiguration of Detroit's green space following the 2008 financial crisis. Finally, through an examination of the role of transition agriculture in post-contact Haudenosaunee land tenure practices, the paper proposes some ways to recover the representation of vegetal world in contemporary situations. The intersections of settler colonial critique and vegetal ontologies produce new avenues for addressing decolonial practices and resistance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.047 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".