Lexical variation of <i>woods</i> and <i>bush</i> in Ontario English
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper examines ongoing lexical variability among words that describe areas with trees, such as woods, bush and forest , among others. The historical perspective shows ongoing semantic evolution of these terms, from wood(s) (c.825) to the emergence of bush in the late 16th century or early 17th century. We assess regional, social and linguistic patterns of variation in 1849 tokens, from individuals born in the late 1800s to early 200s across 21 communities in Ontario, Canada. The most common word is bush ; use of woods is moderate while forest is rare. Ancestry and migration play key roles in their distribution, demonstrating that ancestral roots, migration and language contact play into the selection of a word. We argue that lexical variation, when analysed in a comparative sociolinguistic perspective in the context of social typology, history and geographic location, offers important insights into language use and human behaviour.
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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 | 0.001 | 0.001 |
| 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 it