Seeing and Knowing the Indigenous Peoples of the Americas
Bibliographic record
Abstract
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.
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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 | 0.011 | 0.012 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".