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Record W4256543788 · doi:10.31227/osf.io/2krpt

PENGARUH IKLAN SEPEDA MOTOR YAMAHA TERHADAP CITRA MEREK (STUDI PADA MAHASISWA POLITEKNIK LP3I MEDAN)

2018· preprint· en· W4256543788 on OpenAlexaff
STIM Sukma

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCultural and Artistic Studies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSample (material)AdvertisingBrand imagePopulationTest (biology)PsychologyF-testMathematicsRegression analysisStatisticsBusinessPhysicsSociologyDemography

Abstract

fetched live from OpenAlex

The aim of this research is to know the influence of yamaha motorcycle advertisement on brand image (study on Police student of LP3I Medan). The sample of this study is all members of the population used as a sample, sampling using a saturated sample is a sample determination technique when all members of the population used as a sample. Data analysis using simple regression test with model accuracy (classical assumption test), hypothesis test using coefficient of determination test (R²), partial test (t test), while data processing using SPSS 15. The result of research indicate that advertisement variable can not explain its existence to Brand image, in addition to the partial advertising variable does not affect the brand image.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.066
GPT teacher head0.341
Teacher spread0.275 · 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.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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