Toponymic Constraints in Wemindji
Bibliographic record
Abstract
Research by Eugene Hunn (1996) suggested that toponymic density and population density are roughly equal for a range of indigenous groups across North America. In Wemindji Quebec, historic and current toponymic and population data support Hunn’s observation. I demonstrate that toponymic constraints are real by holding the number of traditional toponyms (898) as a background ‘constant,’ and estimating the growth of Wemindji’s population from 1960 to 2010 based on knowledge held by local experts. Measurements from historic air photographs assumed toponymic growth proportional to the area within the limits of Wemindji town development. 78 new town place names provide a baseline for that measurement. Relative to toponymic density, population density steadily increased from 1960 to 2010, with a graph depicting the two densities suggesting equality in approximately 1995.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".