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Record W2946924426 · doi:10.3968/10981

Politeness Strategies and Address Terms in Igbo and Igala Kinship Cultures

2019· article· en· W2946924426 on OpenAlexvenueno aff
Chinwe Ezeifeka, Joseph Sunday Ojonugwa

Bibliographic record

VenueStudies in literature and language · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitenessIgboKinshipFace (sociological concept)SociologyCivilizationLinguisticsSocial psychologyPsychologyAnthropologyPolitical scienceSocial sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

The work aims at investigating politeness strategies in Igbo and Igala cultures with a view to finding out how these two cultures handle the various strategies, honorifics and address terms in kinship relationships. The theoretical bases of the work are Brown and Levinson’s face-saving view of politeness which draws heavily from Goffman’s concept of face and interaction order. Our findings show that the two cultures under review are conscious of affronts to positive and negative face, favours indirectness and off-record strategies more than bald-on-record strategies. The two cultures also employ culture-specific honorifics and address terms especially in relating with parents, spouses, elder relations, siblings and peers. It is evident from the findings that contrary to what the present day so –called “civilization” may de-culturize people into especially in the use of first names, these two cultures still uphold the inbuilt cultural respect in observing politeness strategies, honorifics and address terms.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0060.005
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.033
GPT teacher head0.340
Teacher spread0.307 · 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
Published2019
Admission routes1
Has abstractyes

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