Dialect Use and Discursive Identities of Migrants from the West in Eastern Germany
Bibliographic record
Abstract
This chapter 1 investigates the connection between dialect use, identity and migration in the German context. In particular, we are concerned with the ways in which individuals from western Germany 2 who moved to the eastern German region of Saxony after the fall of the Berlin Wall in 1989 use the Saxon dialect — which they hear on a daily basis in their everyday surroundings — to make various aspects of their selves and others relevant in the interaction. The process of doing this is also one of relating their individual identities to social categories that are present in their environment. In our analysis, we look specifically at the ways in which these people use the local dialect of their new surroundings -often with other linguistic material — in order to draw attention to the relationship between existing social categories and identities. In contrast with many of the other chapters in this volume, this approach can be characterized as a focus on the ‘lower-case d discourse’ analysis of the German language in interaction, rather than an analysis of the ‘capital D discourses’ which are concerned with the social and political processes that shape the production of German texts (Gee [1999] 2005; see also Cerná, Carl and Stevenson, Horner, this volume). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".