Bibliographic record
Abstract
W My first exposure to the Canadian Rangers came while I was an undergraduate co-op student working at the Department of National Defence for the director general Aboriginal affairs in 1996.One of my first jobs was to help organize the Aboriginal Awareness Week display at National Defence Headquarters.The exhibit consisted of historical photographs and a "living history" display by two Canadian Rangers who were visiting from Northwest Territories.The Rangers built a Styrofoam igloo and, in their quiet and reserved way, explained to military officers and civil servants what they did.As they packed up at the end of the week, one of them handed me a red sweatshirt that had been transported in a crate with a muskox blanket."It smells like sick cow," my then-girlfriend (now wife), Jennifer, who grew up on a dairy farm, told me.She proceeded to work on a master's degree in rural planning and development the next year, and for one of her course papers she examined the proposed Junior Canadian Rangers program as a component of northern community development.I was intrigued and decided that I would someday write the larger history of the Rangers, a success story according to popular media accounts, but one about which so little was known.This book is a living history.It reflects collaboration with the Canadian Ranger Patrol Groups and Rangers from across the country and is grounded in the documentary record, interviews, and participant observation.It reveals how the military and residents of isolated coastal and northern communities
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.280 | 0.117 |
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".