Translating <i>Apȣagana</i>: Conceptual-Affective Modelling as a Tool for Analyzing Historical Indigenous Legal Cultures
Bibliographic record
Abstract
This paper explores the use of conceptual-affective modelling as a powerful tool for understanding Indigenous and European (or Euro-American) interactions in 18th century colonial North America. It illustrates how dictionaries and lexicons of Indigenous languages compiled by colonial actors, particularly the French Jesuit missionaries, can be harnessed to reveal a previously obscure conceptual world. These linguistic sources have only been used sparingly in the social sciences due to the linguistic and structural difficulties in engaging with what are atypical primary sources. Conceptual-affective modelling permits quantitative and qualitative analyses of the concepts contained within these sources. To demonstrate this, the intersection of Indigenous legal cultures and the Calumet [Apȣagana] has been chosen. First, a study of the Calumet in 17th-century Miami-Illinois–and–French dictionaries is discussed. Compiled in the area known to the French as the Pays des Illinois, these were tools for the evangelization project of the Society of Jesus. Within them are a wealth of words, expressions, and phrases that evoke the reality of the members of the political confederation called the Illinois (though their own term for themselves was Inoca). In a period from which few sources recorded Indigenous voices, these open a window onto language and culture. Second, the challenges of translating concepts pertaining to legal culture between societies with vastly different conceptions of justice are examined. The Calumet is considered in relation to a series of translated speeches by Indigenous leaders made in 1723, connected to a case of homicide. Rather than concentrating on the conflicts that can arise from conceptual divergences, the study investigates how attempts to resolve conflicts between different legal cultures can illuminate the diverse attributes that contribute to meaning and communication.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".