“Our Hearts and Brains Are Like Paper, We Never Forget”: Indigenous Petitioning and the World Wars
Bibliographic record
Abstract
Indigenous veterans have been celebrated for their achievements in the two world wars, adding needed texture to Canada’s half-century at war. However, Indigenous peoples on the home front have remained periphery to the study of Indigenous peoples’ experiences of the world wars, leaving veterans and military eligible men as the main protagonists in the story. Those individuals left on reserve experienced the conditions of war, the mobilization of the Canadian state for war, and the enlistment of Indigenous men into the army differently than enlisted men. Analyzing Indigenous petitioners’ political advocacy during the First World War and the Second World War offers a more textured and complex representation of Indigenous peoples’ experiences during the world wars. By negotiating their place within the settler Canadian state, while also clearly defining their sovereignty and distinct political cultures, Indigenous peoples remained active participants in the political arena during the period from 1914 to 1945. Rather than “awakening” politically on the return of veterans in response to broken promises, Indigenous peoples on the home front deployed and evolved existing political tools and strategies to articulate their responses to wartime policy.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".