Defining dialect regions with interpretations: Advancing the multidimensional scaling approach
Bibliographic record
Abstract
In our earlier work, an approach to defining dialect areas using multidimensional scaling (MDS) of the total collection of available raw data (from a region of Romania) has produced results that showed ‘some’ but ‘not all’ of the dialect distinctions that were anticipated. To investigate this situation, we have extended our approach in two ways, one methodological and one technical. Methodologically, we have switched from looking at raw data to examining interpretive maps based on recognized dialect distinctions. Further, we have categorized these interpretations as phonetic (regular and irregular), morphophonemic, morphological, and lexical, examining each category separately. The result is a much clearer set of dialect distinctions, as seen in the MDS pictures. However, the dialect distinctions vary by category, leading us to make suggestions about the role of each category in defining the notion of dialect. Our technical extension is the creation and use of a 3D viewer for looking at the MDS pictures. We project the linguistic-distance space into three, instead of two, dimensions, and manipulate the resulting structure interactively, thus uncovering and eliminating any accidental ‘closeness’, as sometimes happens in the 2D case. Strikingly, the resulting 3D objects seem to be very flat, which strongly suggests that there are only two relevant dimensions for distinguishing these dialects, although the two dimensions do not correspond exclusively to geographic dimensions. The result of these extensions is that the multidimensional approach becomes even more viable as a way of selecting dialect and dialect-transition areas, and perhaps more accessible for use with languages and dialects beyond our own study area.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".