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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".