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Record W2606080436 · doi:10.5539/ass.v13n5p153

Figure of Speech in Bandung Historical Tourism Sites Naming

2017· article· en· W2606080436 on OpenAlexvenueno aff
Eva Tuckyta Sari Sujatna, Kasno Pamungkas, Heriyanto Darsono

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and Language Analysis
Canadian institutionsnot available
FundersDirektorat Jenderal Pendidikan TinggiUniversitas Padjadjaran
KeywordsTourismLinguisticsFigure of speechMetaphorPart of speechLiteral and figurative languageHistoryGeographyPhilosophyArchaeology

Abstract

fetched live from OpenAlex

Many researchers do their research on figurative language or figure of speech, but it is limited research on figure of speech in historical tourism sites naming. The aim of this research is to investigate the figure of speech in historical tourism sites naming in Bandung area. The earlier study explained that the names of tourism destinations in Jawa Barat have different figure of speech and it happens to Bandung historical tourism sites naming. The method used by the present writers in this research is descriptive method. The descriptive method chosen by the present writers is used to identify and classify the names of the historical tourism sites in Bandung area as the data. From the various types of figure of speech referring to the theory, it is found that they are two types of figure of speech found in the data. The two figures of speech employed are personification (Gedung Merdeka and Gedung Indonesia Menggugat) and metaphor (Goa Belanda, Goa Jepang, Paris van Java, and Kota Kembang).

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)0.003
metaresearch head score (Gemma)0.011
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0030.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.276
Teacher spread0.248 · 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
Published2017
Admission routes1
Has abstractyes

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