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Record W4389203950 · doi:10.5430/wjel.v14n1p337

Do They Mind Their Ps and Qs: Politeness Strategies in the Movie, Joy

2023· article· en· W4389203950 on OpenAlexvenueno aff
S. Moorthi, Jayashree Premkumar Shet, D. Solomon Paul Raj, Henry S. Kishore, Mangai Natarajan, Christy Paulina

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsPolitenessPoliteness theoryPsychologyPoliteness maximsInterpersonal communicationLinguisticsSocial psychologyPhilosophy

Abstract

fetched live from OpenAlex

This study was an interpersonal communication study on the politeness methods used by the main characters in the film Joy in the setting mostly of a family cum workplace communication. The purpose of this study was to describe the major characters' politeness tactics. The data for this descriptive qualitative study, the dialogues uttered by all of the movie's principal characters, were scrutinized using Brown and Levinson's (1987) paradigm. The investigation studied how the characters used politeness strategies such as positive politeness, Bald-on -Record, negative politeness and off-record techniques in their utterances. The data came from the script of Joy, and it was identified that 833 politeness strategies were employed. Bald on Record (8%), Positive Politeness (51%), Negative Politeness (35%), and Off Record (6%). The findings revealed that positive politeness strategies were the most frequently used in this film. The results indicated that the main characters had a tendency to use positive politeness to show their respect and also to maintain a harmonious relationship in the family and workplace.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.262
Teacher spread0.250 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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