Bibliographic record
Abstract
Tucked away in the northeastern corner of Alaska is one of the most contested landscapes in all of North America: the Arctic National Wildlife Refuge. Considered sacred by Indigenous peoples in Alaska and Canada and treasured by environmentalists, the refuge provides life-sustaining habitat for caribou, polar bears, migratory birds, and other species. For decades, though, the fossil fuel industry and powerful politicians have sought to turn this unique ecosystem into an oil field. Defending the Arctic Refuge tells the improbable story of how the people fought back. At the center of the story is the unlikely figure of Lenny Kohm (1939–2014), a former jazz drummer and aspiring photographer who passionately committed himself to Arctic Refuge activism. With the aid of a trusty slide show, Kohm and representatives of the Gwich’in Nation traveled across the United States to mobilize grassroots opposition to oil drilling. From Indigenous villages north of the Arctic Circle to Capitol Hill and many places in between, this book shows how Kohm and Gwich’in leaders and environmental activists helped build a political movement that transformed the debate into a struggle for environmental justice. In its final weeks, the Trump administration fulfilled a long-sought dream of drilling proponents: leasing much of the Arctic Refuge coastal plain for fossil fuel development. Yet the fight to protect this place is certainly not over. Defending the Arctic Refuge traces the history of a movement that is alive today—and that will continue to galvanize diverse groups to safeguard this threatened land.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".