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The Natural History of Colonial Science: Joseph-François Lafitau's Discovery of Ginseng and Its Afterlives

2016· article· en· W2266061530 on OpenAlexaboutno aff
Christopher M. Parsons

Bibliographic record

VenueThe William and Mary Quarterly · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGinseng Biological Effects and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMohawkIndigenousColonialismTraditional knowledgeIndigenous cultureEnvironmental ethicsHistoryScientific discoveryProduct (mathematics)EthnologyPolitical scienceArchaeologyEcologyPsychologyPhilosophyBiology

Abstract

fetched live from OpenAlex

The Jesuit Joseph-François Lafitau announced his 1716 discovery of ginseng outside Montreal as the product of his encounter with Mohawk women. While Lafitau’s efforts to foreground the participation of indigenous peoples have often been lauded, the manner in which he laid claim to the discovery was integral to the subsequent rise of an ecologically unsustainable and culturally disruptive trade in ginseng. When Parisian naturalists argued that he had found an unrelated plant, Lafitau produced a detailed examination of indigenous ecological and medical knowledge. His insistence on the existence of an Asian plant in North America emerged from his larger ambitions to demonstrate the Old World origins of indigenous peoples. While he succeeded in winning over his detractors, he disseminated a body of knowledge about indigenous plant collection that most actively interested merchants. These merchants translated Lafitau’s insights regarding indigenous cultures into a global trade that was economically profitable but locally devastating. The consequences of Lafitau’s discovery therefore suggest a need for both attention to the material impacts of intellectual endeavors and caution as today’s Native Americans are again being asked to collaborate with academics to better know and protect American environments.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.401

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 studies0.0000.001
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 head0.005
GPT teacher head0.215
Teacher spread0.210 · 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 designBench or experimental
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

Citations11
Published2016
Admission routes1
Has abstractyes

Explore more

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