The Monopoly System of Wildlife Management of the Indians and the Hudson's Bay Company in the Early History of British Columbia1
Bibliographic record
Abstract
During the centuries before the white man's arrival in British Columbia, the native peoples, belonging to ten linguistic groups and numbering from 8o,ooo to 125,000, * developed a system of land tenure that provided the base for effective management of major animal, fish and plant resources needed for a livelihood. Because this system lasted well into the European/Canadian fur-trade era, there is considerable documentation of it and of the monopoly wildlife management practised by the Indians of British Columbia at the time of contact. Each of the ten Indian language groups divided into several bands or tribes who separately held a generally well-defined territory, the sover-eignty of which was recognized by neighbouring tribes. Tribal territory, in turn, divided into hunting territories and fishing sites, the possessor)7 rights of which were held and strictly guarded by a clan, a smaller family group or even an individual. Usually these rights were handed down from one generation to another. This monopoly control provided the essential conditions upon which the efficient management of important resources could be implemented. The recorded evidence of Indian owner-ship of hunting and fishing grounds in British Columbia covers the entire province.3 1 This article is an extraction of the author's MA thesis, "A History of Wildlife
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".