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Record W2992880425 · doi:10.1075/prag.19022.hor

Urban interaction ritual

2019· article· en· W2992880425 on OpenAlexafffund
Mervyn Horgan

Bibliographic record

VenuePragmatics Quarterly Publication of the International Pragmatics Association (IPrA) · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIncivilityTypologySociologyFraming (construction)Public spaceSocial psychologyUrban spaceCivilityPsychologyValue (mathematics)PoliticsAnthropologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Abstract Most encounters between strangers in urban public spaces involve the ritual of civil inattention ( Goffman 1963 ). Generalized diffusion of this ritual upholds the urban interaction order. This article outlines a typology of infractions of the ritual of civil inattention, and focuses on two types: uncivil attention and uncivil inattention. Drawing on interviews (n = 326) about participants’ most recent encounter with a rude stranger in urban public space gathered by the Researching Incivilities in Everyday Life (RIEL) Project, variations between verbally, physically, and gesturally initiated incivilities are examined. Data suggests a correlation between types of initiating move and subsequent verbal exchange. Analysis demonstrates the value of ritual framing for understanding interactional conflict between strangers, and indicates that the broader concept of incivility can supplement and extend existing impoliteness research by encompassing both linguistic and non-linguistic forms of interactional conflict.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0030.002
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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.

Opus teacher head0.016
GPT teacher head0.253
Teacher spread0.238 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2019
Admission routes2
Has abstractyes

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