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Record W7099885904

The Monopoly System of Wildlife Management of the Indians and the Hudson's Bay Company in the Early History of British Columbia1

2016· article· en· W7099885904 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsnot available
Fundersnot available
KeywordsMonopolyFishingWildlifeWildlife managementWhite (mutation)Commercial fishingGovernment (linguistics)DocumentationNatural resource
DOInot available

Abstract

fetched live from OpenAlex

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

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.007
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.016
GPT teacher head0.198
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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