Beyond the Isogloss: The Isograph in Dialect Topography
Bibliographic record
Abstract
Isoglosses do not accurately reflect the patterns of linguistic use in a geographical region, because the isolated conservative forms they are based on fail to represent the actual variants extant in the population. I have developed a new dialect atlas tool, the isograph, that will enable researchers to quickly find dialect trends in more representative data sets. Using Canadian and American data from the Dialect Topography database, I show how isographs can illuminate our understanding of linguistic boundaries at the provincial, national, and cross-border levels. Unlike dialect geography, dialect topography collects data from people of all ages and backgrounds, and provides a multidimensional picture of how variants are used in a community (Chambers 1994, 1998). Because variants occur in different proportions in each community, analysis is necessarily quantitative, which necessitates abandoning the traditional isogloss, as discrete datum points common to dialect geography are no longer available. The isograph maps similarities between regions by comparing adjacent regions and plotting potential channels for language spread between the regions (Figure 1). For each region, percentage differences from its neighbours are calculated, and a line is drawn between it and its neighbour(s) with the least difference. When all lines of minimum distance have been drawn for all regions, the result is a constellation-like pattern that clearly groups together the most similar regions (e.g., zee/zed, Figure 2). While statistical analyses remain the most reliable way to group together similar regions, the isograph method will provide a rapid picture of gross linguistic differences. I analyze the isographs of 29 phonological and pronunciation variables from the Dialect Topography of Canada using the on-line database and atlas and an isograph program. From the
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".