Bibliographic record
Abstract
The militarization of Alaska during and after World War II created an extraordinary set of new facilities. But it also reshaped the imaginative role of Alaska as a hostile environment, where an antagonistic form of nature could be defeated with the appropriate combination of technology and training. One of the crucial sites for this reformulation was the Arctic Aeromedical Laboratory, based at Ladd Air Force Base in Fairbanks. In the first two decades of the Cold War, its employees conducted numerous experiments on acclimatization and survival. The laboratory is now best known for an infamous set of tests involving the application of radioactive tracers to indigenous Alaskans--experiments publicized by post-Cold War panels established to evaluate the tragic history of atomic-era human subject research. But little else has been written about the laboratory's relationship with the populations and landscapes that it targeted for study. This essay presents the laboratory as critical to Alaska's history and the history of the Cold War sciences. A consideration of the laboratory's various projects also reveals a consistent fascination with race. Alaskan Natives were enrolled in experiments because their bodies were understood to hold clues to the mysteries of northern nature. A scientific solution would aid American military campaigns not only in Alaska, but in cold climates everywhere.
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.016 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".