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Record W4214574804 · doi:10.46827/ejals.v4i2.317

APOLOGY STRATEGIES IN CAMEROON FRENCH

2022· article· en· W4214574804 on OpenAlexaff
Bernard Mulo Farenkia

Bibliographic record

VenueEuropean Journal of Applied Linguistics Studies · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsCape Breton University
Fundersnot available
KeywordsSpeech actPsychologyTask (project management)LinguisticsSocial psychologyPhilosophyEconomicsManagement

Abstract

fetched live from OpenAlex

This article discusses the results of a case study on strategies used by Cameroon French speakers to apologize in situations involving friends and superiors. The data of the study were collected by means of a Discourse completion Task Questionnaire that was administered to two groups of university students. The findings show that the participants used a wide range of direct and indirect apology strategies and that the apology utterances mostly occurred in speech act sets, which generally involved combinations of direct and indirect apologies and supportive acts. The results also reveal the use of nominal address terms, codeswitching and some indigenized patterns of French to modify the illocutionary force of apologies. Overall, the linguistic and pragmatic choices made by the respondents varied according to degree of familiarity and power distance between the interlocutors. Article visualizations:

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)0.004
metaresearch head score (Gemma)0.012
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 score0.040
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.005
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
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 head0.054
GPT teacher head0.286
Teacher spread0.232 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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