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Record W4319782426 · doi:10.3726/b19383

Seeing and Knowing the Indigenous Peoples of the Americas

2023· book· en· W4319782426 on OpenAlexaboutno aff

Bibliographic record

VenuePeter Lang Verlag eBooks · 2023
Typebook
Languageen
FieldSocial Sciences
TopicColonialism, slavery, and trade
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousFur tradeAllianceFaithFellNegotiationHistoryEconomyEconomic historyPolitical scienceGeographyEthnologyLawCartographyEconomics

Abstract

fetched live from OpenAlex

When the Norman and Breton armateurs sent their ships to the New World in the sixteenth century, they had faith that through the ability to negotiate with the Indigenous peoples with whom they sought to trade, the leaders of these expeditions would return to Saint-Malo or Dieppe with precious cargo. Among these were brazilwood (used to dye cloth), chinaroot (to relieve symptoms of the pox), and furs for the European market. Storms or attacks by hostile vessels could destroy or reduce the value of the profit, but over the years the financial return proved advantageous. How and why this risky but profitable venture fell into the hands of Breton and Norman financiers lies at the heart of our story. The consequences of their investment in Brazil, Canada, and Florida would change the world, and the strategies used by the merchant mariners they sent out were key to the success of their enterprise. Seeing and Knowing the Indigenous Peoples of the Americas: Exchange and Alliance Between France and the New World During the French Wars of Religion is the first analysis of accounts or relations by French naval expeditions to focus on specific strategies of encounter and trade from Canada to Brazil, including the area of Florida and South Carolina. Since the expeditions took place during the French Wars of Religion an effort is made to examine how differences of religion and character affected the success of the alliance and exchange. The work is suitable for inclusion in undergraduate/graduate French, history, cultural studies, or anthropology courses.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.975
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.278
Teacher spread0.254 · 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 teacher head, 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
Published2023
Admission routes1
Has abstractyes

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