Political Ecologies of Race: Settler Colonialism and Environmental Racism in the United States and Canada
Bibliographic record
Abstract
Abstract Drawing inspiration from popular efforts to connect a wide array of political struggles, this symposium examines the ways that racial‐colonial politics unfold through nature and environmental practices linking past, present, and future across the United States and Canada. By way of introduction, we ask: What does it mean to do political ecologies of race in Canada and the United States? For us the response cannot be additive—merely grafting attention to racial/colonial politics onto established scholarly conventions. Instead, we aim for a deeper analysis that challenges and enlivens the field of political ecology. This introduction highlights what is at stake, and identifies the ways that the contributors' research pushes the field. Ultimately, we argue that political ecologies of race can help reinvigorate intellectual projects and build liveable futures by recognising and supporting the connections between ongoing struggles. We hope this symposium contributes to the task.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".