Nature unbound: what gray wolves, monarch butterflies, and giant sequoias tell us about large landscape conservation
Bibliographic record
Abstract
This dissertation examines how and why people of different nationalities across North America cooperate, or not, in conserving transborder ecologies. This project is important because many species of wildlife have been moving across administrative and national borders to cope with a warming world. Out of four thousand species recently tracked, scientists documented that almost three-quarters of them had shifted their ranges, mostly to cooler lands and waters. Terrestrial species, on average, were moving 12 miles (20 kilometers) toward the poles every decade. As the world heats up, threatened biota need more freedom of movement, greater flexibility with borders, to adapt and adjust. My research objective became to recover a useable past about three focal species--gray wolves, monarch butterflies, and giant sequoias--reflecting how these lifeforms were pivotal to the making, unmaking, and remaking of borders for a layering process, a thick cartography, in written word. Conserving large landscapes for each species takes us outside the international lines of modern maps, from the U.S.-Canada border, to the U.S.- Mexico border, to the treaty borders of Indigenous nations subsumed within the United States. My argument is that state-centered conservation followed the possessive logic of nation-building, creating borders and bounding space to protect habitats. New scientific practices such as radio-collaring wolves, tagging monarchs, and tree-ring dating sequoias rendered visible non-human geographies that did not fit the shape or size of traditional protected areas. Civil society in Canada, Mexico, and United States then rallied behind alternative ways of organizing space, building transnational connections for biological well-being. In short, I investigate how non-state actors on the community level reconciled legal, administrative, and national borders with biocentric borders over the long twentieth century (1850s to present). Civic groups like the binational Yellowstone-to-Yukon Initiative, trinational Insect Migration Association, and multinational Indigenous Fire Collective arrived at a political imaginary in-the-making that I call 'ecological internationalism.' Once recognized, its strategy becomes obvious: forge solidarity across borders or face extinction of shared species. Ecological internationalism offers us both a version of the past and a vision of the future.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".