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Record W7162002165 · doi:10.3138/topia-2025-0016

Indigenous Perspectivism and the Anti-Cartesian Animals of Lahontan and Leibniz

2025· article· en· W7162002165 on OpenAlexaffvenue
Adam Lauder

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Philosophy and Science
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsPerspectivismAnthropocentrismSkepticismSocialityIndigenousPerceptionIntervention (counseling)The Imaginary

Abstract

fetched live from OpenAlex

Baron de Lahontan’s cosmopolitical representations of the republic of beavers recounted to him by Indigenous interlocutors constitute a knowing intervention within period debates about the intelligent sociality of animals. Emulating earlier thinkers of the animal such as Montaigne, Lahontan drew on the techniques of philosophical skepticism to communicate the teachings of his Abenaki, Anishinaabeg, Meskwaki and Wendat hosts to European readers. Ancient skeptic thinkers had mobilized the apparent sensorial intelligence of animals to undermine the anthropocentric bias of Stoic constructions of reason. Reviving and adapting such tactics to communicate his encounter with Indigenous knowledge to an early eighteenth-century readership, this article argues that Lahontan’s writings shaped the philosopher G.W. Leibniz’s choice of ecological and animal metaphors for the cosmic perceptual capacities of the “monad” proposed by his late writings.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.039
Scholarly communication0.0050.004
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.254
Teacher spread0.216 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractyes

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