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Record W4392022017 · doi:10.51644/9780889208391

Rupert’s Land

2006· book· de· W4392022017 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook
Languagede
FieldEconomics, Econometrics and Finance
TopicHistorical Economic and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyHistory

Abstract

fetched live from OpenAlex

For nearly two centuries, the Company of Adventurers trading into Hudson’s Bay exported from Rupert’s Land hundreds of thousands of pelts, leaving in exchange a wealth of European trade goods. Yet opening the vast northwest had more far-reaching effects than an exchange of beaver and beads. Essays by a dozen scholars explore the cultural tapestry woven by explorers, artists, settlers, traders, missionaries, and map makers. Richard Ruggles traces the mapping of the territory from the mysterious gaps of the 1500s to the grids of the nineteenth century. John L. Allen recounts how fur-trade explorations encouraged Thomas Jefferson to dispatch the Lewis and Clark expedition. Irene Spry retells the gusto with which John Palliser, a half-century later, studied the prairies. Olive Dickason examines the first contacts of Europeans with Inuit and Amerindians, while James G.E. Smith presents the differing views of the land held by Caribou Eater Chipewyan and traders. Robert H. Cockburn, following Oberholtzer in 1912 and Downes in 1939, finds two more recent views of the Caribou Eater Chipewyan. Fred Crabb points out that much of this century’s church work has been carried out by native and mixed-blood residents. Clive Holland outlines Franklin’s first land expedition. Sylvia Van Kirks clerk in the trade finds his opinion of “this rascally and ungrateful country“ gradually changing, while R. Douglas Francis compares the ideal image and reality as the West opened to settlement. Robert Stacey tells how the theories of the picturesque and the sublime influenced artists portrayals of the West and the Arctic; Edward Cavell illustrates how the camera recorded Rupert’‘s Land and changed our perceptions of it as well. Forty-six maps, drawings and paintings, and documentary photographs illustrate the tapestry of the text.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.974
Threshold uncertainty score0.577

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1730.052

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 head0.029
GPT teacher head0.188
Teacher spread0.159 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2006
Admission routes1
Has abstractyes

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