MétaCan
Menu
Back to cohort
Record W4288422997 · doi:10.5430/wjel.v12n6p220

Thematic Development Analysis on Sunday Sermon Texts in Batak Christian Protestant Church

2022· article· en· W4288422997 on OpenAlexvenueno aff
Hiace Vega Fernando Siahaan, Eddy Setia, Amrin Saragih, Ridwan Hanafiah

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldComputer Science
TopicEnglish Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsSermonThematic mapThematic analysisProtestantismThematic structureResearch developmentSociologyLiteratureLinguisticsQualitative researchArtTheologyPhilosophySocial scienceGeographyCartographyTest (biology)

Abstract

fetched live from OpenAlex

This research article investigates thematic development used on Sunday sermon texts in Batak Christian Protestant Church that were delivered by the preachers. Based on systemic functional linguistics with the framework of textual function, this research article focus on analysing how the thematic development constructed and construed on Sunday sermon texts. In addition, this research article looks and discusses how the preachers used and adapted the thematic development in delivering the Sunday sermon texts. This study was a descriptive qualitative. Model interactive data analysis was used in analysing the data. There were seventeen texts of Sunday sermon texts that were analyzed in this research article. The findings of this research article showed that only five patterns of thematic developments that were found from eight patterns of thematic development. This research article highlights how the usage of thematic development in delivering the Sunday sermon texts. It is also recommended that another preachers use thematic development in delivering their sermon to the congregation in understanding the sermon they delivered.

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)0.008
metaresearch head score (Gemma)0.013
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 score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0050.004
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.241
Teacher spread0.233 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of English LanguageSame topicEnglish Language Learning and TeachingFrench-language works237,207