Bibliographic record
Abstract
Journalist James Brooke explained to readers that Indians across the continent were hard at work 'Stalk [ing] a Silent, Deadly Enemy in the Prairie' -the dangerous residue of militarism in their own backyards: 'It is a little known footnote to the military history of North America that when wars loomed in the 20th century, military planners in Canada and the United States repeatedly turned to the native peoples of the West and took control, through leases or outright expropriation, of large swaths of land for bombing ranges ... Brian Lloyd, a former British Army bomb-disposal expert who directs cleanup operations here, said: "In Canada, the military acted like a giant, using Indian land like stepping stones across the country.You find an Indian nation, and you find range contamination."'For members of affected communities, the use of their Indian reserves and traditional territories for military training represented more than a footnote.It is part of a 'treadmill of destruction' tying militarism to environmental and political injustice. 1 With hundreds of millions of dollars involved in outstanding Aboriginal land claims against the military, and after decades of confrontation, the effects of past military activities continue to be felt in the present.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.549 | 0.319 |
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".