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Record W2554574181 · doi:10.1111/tger.12011

Why a German ‘oh’ is not necessarily an English ‘oh’: Showing understanding and emotions with Change‐of‐State Tokens

2016· article· en· W2554574181 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDie Unterrichtspraxis/Teaching German · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsGermanSecurity tokenConversationState (computer science)Session (web analytics)Computer scienceLinguisticsPsychologyTransition (genetics)CommunicationWorld Wide WebProgramming languageComputer security

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.317
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it