Hunting Caribou: Subsistence Hunting along the Northern Edge of the Boreal Forest
Bibliographic record
Abstract
"Denesuline hunters range from deep in the boreal forest far into the tundra of northern Canada. Henry S. Sharp, a social anthropologist and ethnographer, spent several decades participating in fieldwork and observing hunts by this extended kin group. His daughter, Karyn Sharp, who is an archaeologist specializing in First Nations Studies and is Denesuline, also observed countless hunts. Over the years the father and daughter realized that not only their personal backgrounds but also their disciplinary specializations significantly affected how each perceived and understood their experiences with the Denesuline. In Hunting Caribou, Henry and Karyn Sharp attempt to understand and interpret their decades-long observations of Denesuline hunts through the multiple disciplinary lenses of anthropology, archaeology, and ethnology. Although questions and methodologies differ between disciplines, the Sharps' ethnography, by connecting these components, provides unique insights into the ecology and motivations of hunting societies. Themes of gender, women's labor, insects, wolf and caribou behavior, scale, mobility and transportation, and land use are linked through the authors' personal voice and experiences. This participant ethnography makes an important contribution to multiple fields in academe while simultaneously revealing broad implications for research, public policy, and First Nations politics"..
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".