Unsettling Race, Nature, and Environment in Geography
Bibliographic record
Abstract
What might it mean to “unsettle” our disciplinary understanding of race, nature, and the environment? In this introduction to the 2023 Special Issue of the Annals of the American Association of Geographers—focused on Race, Nature, and the Environment—we reflect on the meaning and practice of unsettling in a time of climate crisis, toxic legacies, uneven development, state violence, mass extinctions, carceral logics, and racial injustices that shape—and are shaped by—the (re)production of nature. We note the ascendancy of critical scholarship on race and racialization in Anglo-American geography; its uneven diffusion and unmet challenges; and the unstoppable force of insurgent thinking, abolition geography, critical race theory, Black and Indigenous geographies, scholar activism, and environmental justice praxis in taking hold and transforming the discipline. The sixteen articles in this special issue embody different ways to “unsettle” disciplinary thought across the vibrant fields of political ecology and human–environment geography. We discuss how the articles collectively grapple with timely questions of land, water, territory, and place-making; render visible the spatial and socioecological reproduction of power and violence by capital and the state; and make space for the enduring politics of struggle on multiple registers—body, home, classroom, park, city, community, region, and world.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".