Bibliographic record
Abstract
Introduction Southern American English (SAE) has long been regarded as a conservative variety preserved in large part by the rural, insular character of the region. As a result, until recently most researchers have attempted to explain the distinctive character of SAE by focusing on its settlement history and its roots in the various regional dialects of Great Britain. At its worst, the view of SAE as a conservative variety and the focus on British roots has led to the assertion that SAE is pure Elizabethan or pure Shakespearean English. At its best, it has led to the kind of careful research exemplified by Michael Montgomery's (1989b) exploration of the connections between the patterns for the use of verbal - s in southern Appalachia and those in northern Britain. While the work of scholars like Montgomery has helped clarify the origins of some SAE features, a growing body of research over the last ten years has shown that many other characteristics of SAE cannot be traced to British roots or correlated with settlement history. In fact, this research suggests that many of the prototypical features of SAE either emerged or became widespread during the last quarter of the nineteenth century or later and that many older SAE features have been disappearing rapidly. The ultimate consequence of such research is that innovation and change, rather than preservation and stability, may well be the most important factors in the development of SAE.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".