Words are Monuments: Racism and Colonialism Conserved in Place Names
Bibliographic record
Abstract
Conservation science aims to improve human wellbeing through environmental management, but the discipline must reckon with the living legacies of its history including racism and colonialism. US national parks are symbolic of conservation and ripe for examination for their contribution to socio-spatial exclusion of Black, Indigenous and other people of color from outdoor spaces. We examined the origins of over 2,000 place names in 16 (26% of) US national parks to quantify the extent that national park narratives perpetuate colonialism and racism. Through iterative thematic analysis of place name origins we constructed a decision tree for classifying place name problem types according to their dimensions of racism and colonialism (if any), which enabled quantification and spatial analysis of problem types by park. We found that these highly visible conservation landscapes commemorate individuals and words that tacitly endorse racist and anti-Indigenous ideologies at a system scale. Changing these names is one small step towards dismantling colonialist and racist narratives and towards making public conservation landscapes more inclusive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".