Why a German ‘oh’ is not necessarily an English ‘oh’: Showing understanding and emotions with Change‐of‐State Tokens
Bibliographic record
Abstract
This paper presents a two‐session teaching unit on German change‐of‐state tokens such as oh, ach and achso. Goal is to teach students the appropriate reaction through change‐of‐state tokens in various situations. Students are provided with authentic data based on empirical research in conversation analysis (CA). By the end students will be familiar with the German change‐of‐state tokens and be aware of the difficulties of translating oh, who often is a false friend. They will be able to differentiate between a cognitive and an emotional change‐of‐state token and know where the English oh cannot be translated with an German oh. The unit focuses on ach, achso and oh in the German language and helps students to sound more fluent in their second language since the tokens glue speech together. For this reason, it is important for students to learn how to use change‐of‐state tokens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".