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Record W1981255791 · doi:10.5430/elr.v1n2p88

Misinterpretations in English-Kimuthambi Church Sermons

2012· article· en· W1981255791 on OpenAlexvenueno aff
Ireri Humphrey Kirimi, Muriungi Kinyua Peter, Njogu Waita

Bibliographic record

VenueEnglish Linguistics Research · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican history and culture analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsBantu languagesInterpretation (philosophy)PragmaticsRendering (computer graphics)Computer scienceNounHistoryPhilosophyArtificial intelligence

Abstract

fetched live from OpenAlex

Interpretation is a complex task for it involves the rendering of an oral message from one language to another simultaneously. This paper looks at misinterpretations in English - Kimuthambi church sermons in Muthambi Division, Maara District - Kenya. Kimuthambi is a Bantu language spoken in Kenya. The paper lists misinterpretations in English - Kimuthambi church sermons, identifies words and phrasal categories frequently misinterpreted in English - Kimuthambi church sermons and then provides some explanations to the misinterpretations. The paper establishes that there are misinterpretations in English - Kimuthambi church sermons. It presents the findings which show that that verbs and verb phrases are the most frequently misinterpreted categories followed by the nouns and noun phrases. The findings in this paper contribute to the scholarly literature in interpretation and translation which traverses different levels of language study like translation, discourse analysis, semantics and pragmatics.

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)0.006
metaresearch head score (Gemma)0.024
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.007
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.075
GPT teacher head0.409
Teacher spread0.334 · 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
Published2012
Admission routes1
Has abstractyes

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