Bibliographic record
Abstract
In the United States, the federal government owns more than a quarter of the nation’s landscape—nearly 640 million acres; or more than a million square miles, which, if consolidated, would make it the tenth largest nation on earth. Primarily managed by four federal agencies—the Bureau of Land Management, the U.S. Forest Service, the U.S. Fish and Wildlife Service, and the National Park Service—American public lands have been central to developing the American economy, state, and identity. The history of these lands intersects with critical components of the American past—namely nature, politics, and economics. From the beginning, the concept of “public” has been the subject of controversy, from visions of homesteaders realizing the ideal of the Jeffersonian republic to western ranchers who use the open range to promote a free enterprise system, to wilderness activists who see these lands as wild places, free from human encumbrance. Environmental historian Adam Sowards synthesizes public lands history from the beginning of the republic to recent controversies. Since public lands are located everywhere, including iconic national parks like Yellowstone or the Grand Canyon, Americans at large have a stake in these lands. They are, after all, ours. In a real sense, this book is for those citizens who camp in the national forests, drive through the national parks, or admire distant wilderness landscapes. These readers will gain a greater appreciation for the long and complex history of the range of these places.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".