MétaCan
Menu
Back to cohort
Record W4416527252 · doi:10.1016/j.pragma.2025.10.008

Responding to new information with negative discourse particles nein/nee/nö in German talk-in-interaction

2025· article· en· W4416527252 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

VenueJournal of Pragmatics · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsConceptualizationConversationGermanEmbodied cognitionFunction (biology)Conversation analysis

Abstract

fetched live from OpenAlex

This conversation analytic and interactional linguistic study examines how the negative discourse particles nein/nee/nö (‘no’) are used in response to new information in mundane and institutional talk-in-interaction in spoken German. We analyze two functions of these responsive negative discourse particles: First, we examine how these particles serve to receipt information as new and display a stance toward it, e.g., an affiliative negative stance or surprise. Second, we explore how they can function to request reconfirmation, treat the prior informing as new, and/or invite further elaboration – or even reconsideration – of what another person previously said. Our study describes responses to news that have not been featured in prior research and shows how different design features of nein/nee/nö -turns disambiguate their function, shape interactional trajectories, and manage epistemic and affective positioning. We also uncover how nein/nee/nö -turns can be used in response to a visually perceived event or state of affairs and discuss the implications of such uses for our conceptualization of “information” and “news”.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.509
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
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 head0.024
GPT teacher head0.343
Teacher spread0.319 · 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