Naming Place in Kanyen’kéha: A Study Using the O’nonna Three-Sided Model
Bibliographic record
Abstract
Human interactions with place are the stuff of life and aspects of place such as landscape and environment have shaped human activity since activity could be considered "human".Why and how we choose to name place, as well as which places are named or nameless provides insight into many of the different aspects of life, from knowledge of resources of the area, navigational information, and knowledge of significant events in the vicinity (Afable and Beeler, 1996, Stewart, 1975) to the movement of people across a landscape, their value systems, and even spirituality.Furthermore, recent work by Levinson, Burenhult, Mark and others demonstrates that the division of landscape is not universal, but rather is shaped by linguistic and cultural practices.Some place names encode these differing views of the delineation of landscape.This dissertation argues that place names lie at the intersection of landscape, language and culture and outlines a new interdisciplinary philosophical framework and methodology for their study which draws from the fields of linguistics, geography and anthropology for their examination.Together with members of the Kanyen'kehá:ka, this framework and methodology, called the O'nonna Three-Sided Model, are used to explore the relationship of the Kanyen'kehá:ka to their landscape.In analyzing the meaning of the lexical semantics of Kanyen'kéha place names, patterns emerge which provide insight into Kanyen'kehá:ka geography and culture.In the discussion, I demonstrate how these patterns can be viewed in different ways demonstrating why the three components of language, landscape and culture are vital to form a holistic picture of the way that people name place.Patterns also emerge in the grammar of place names and I show how close examination of these patterns, and the linguistic mechanisms used to describe place, may lead to surprising conclusions that may not have been obvious at first glance.Finally, I show how the dual components of meaning and grammar of place names provide insight into cognition, linguistic relativity and the universality of the human experience.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".