Measuring the New World: Enlightenment Science and South America
Bibliographic record
Abstract
Prior to 1735, South America was largely terra incognita to many Europeans. But that year, the Paris Academy of Sciences sent a joint French and Spanish mission to the Spanish American province of Quito (in present-day Ecuador) to study the curvature of the Earth at the Equator - an expedition that would put South America on the map and in the minds of Europeans for centuries to come. Equipped with quadrants and telescopes, the mission's participants referred to the transfer of scientific knowledge from Europe to the Andes as a sacred fire passing mysteriously through European astronomical instruments to curious observers in South America.By looking at the social and material traces of this expedition, Measuring the New World examines the transatlantic flow of knowledge in reverse - from West to East. Through ephemeral monuments and geographical maps, from the Andes to the Amazon River, the book explores how the social and cultural worlds of South America contributed to the production of European scientific knowledge during the Enlightenment. Neil Safier uses the notebooks of traveling philosophers, including Charles-Marie de La Condamine and others, as well as maps and specimens from the expedition, to place this particular scientific endeavor in the larger context of early modern print culture and the emerging intellectual category of scientist as author.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".