Wortgeografischer Wandel im Schweizerdeutschen. Sommersprossen, Küchenzwiebel und Schmetterling 70 Jahre nach dem SDS
Bibliographic record
Abstract
This paper intends to show the importance of having linguistic instruments, principally semantic ones, for determining the meaning of words with the greatest precision and, consequently, managing to meticulously establish the different meanings of a dictionary's entry words. As an example, a new definition of the Spanish verb mezclar ('to mix') will b Since the beginning of the publication of the linguistic atlas of German-speaking Switzerland (Sprachatlas der deutschen Schweiz, SDS) in the early 1960s individual linguists collected contemporary material for comparison to investigate language change. However, due to time and money restrictions these studies were limited to small parts of the language area only. So far a description of tendencies concerning the entire Swiss German language area is missing. Based on an online-survey of 5600 informants this investigation is the first to present word geographic data covering (almost) the whole German-speaking Switzerland. Comparing GIS-maps of SDS and online data of the dialectal lexemes for freckles, onion and butterfly, language change over the last century becomes apparent, with striking convergence tendencies towards standard German, but also a Swiss German dialect expanding its range. Most of the dialect words mentioned in the SDS were preserved; some new were found. Thus, diversity of lexicon and creative language use are not endangered. Statistical analysis showed that younger speakers are more likely to deviate from the SDS. Less strong, but still significant were the influence of the parent's dialect and the duration of living in the dialect area, whereas gender had no influence.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".