Dialectology as "language making": Hegemonic disciplinary discourse and the One Standard German Axiom (OSGA)
Bibliographic record
Abstract
This paper problematizes the current anti-pluricentric perspectives in German dialectology in the context of “language making” (Krämer et al. 2022). Disciplinaryhistory and cross-linguistic comparison shed light on what appears to be discipline-internal theoretical hegemony on what makes a language and what a dialect. Thepaper proposes the existence of a long-standing, discipline-defining One StandardGerman Axiom (OSGA) to be operative, an axiom that “unmakes” non-dominantstandard varieties. It will be shown that, given the unbroken chain of tradition inGerman dialectology (via, e.g. Kranzmayer or Mitzka) based on Germanic Stämme(‘tribes’), the concept of “German language” is a priori defined as a stand-alone sin-gle entity. A comparison between Stämme in German literature – now obsolete –and Stämme in German dialectology – still strong – illustrates the far-reachingramifications of OSGA. Three fail-safes are suggested to move the debate onto anepistemologically sounder footing and to allow for the dynamic, in part predictable,development of multiple linguistic standards in German via Pluricentric Theory(Multi-Standard Theory). Pluricentric Theory remains, it is argued, the theory ofchoice, though the present paper extends Clyne’s (1995) model with transnationalcross-linguistic 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".