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Record W4367189191 · doi:10.5539/elt.v16n5p77

Pragmatic Analysis of Selected Speeches of Some Governors on Workers’ Day Celebration in Nigeria

2023· article· en· W4367189191 on OpenAlexvenueno aff
Isaiah I. Agbo, Rejoice C. Nwachukwu, Walter O. Ugwuagbo

Bibliographic record

VenueEnglish Language Teaching · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsGovernorNonprobability samplingAssertivenessPsychologyState (computer science)PoliticsSociologySocial psychologyPublic relationsPolitical scienceLaw

Abstract

fetched live from OpenAlex

This study focuses on the pragmatic analysis of selected speeches made by some governors on Workers’ Day Celebration in Nigeria. The study aimed to identify and analyze the pragmatic features of the speeches and to establish how these features reveal the governors’ intentions. Purposive sampling method was used to select and transcribe two recorded speeches of Governor Emmanuel Udom (2019) of Akwa Ibom State and Governor Seyi Makinde (2020) of Oyo State. Qualitative research design was used to analyze the transcribed texts. The theoretical models of Speech Acts by Austin (1962) and Searle (1969) were deployed in the analysis. The findings revealed that the pragmatic features in the speeches were conveyed through the following illocutionary acts: assertive (33%), commissive (20%), declaratives (15%), executives (13.75%), expressive (10%), and directives (7.5%). The study also establishes that the governors’ intentions were to prioritize workers’ welfare, encourage and assure them of their interest and sustain the post-COVID 19 economy. The study concluded that the pragmatic features of the speeches effectively conveyed the governors’ intentions and motives, thus highlighting the importance of language and ideology in political communication.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.031
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.275
Teacher spread0.258 · 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 teacher head, 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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