Lexical variation of <i>woods</i> and <i>bush</i> in Ontario English
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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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".