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Record W4402283052 · doi:10.35906/equili.v13i2.2055

STRATEGI INOVASI DALAM PEMENANGAN PEMILU PRESIDEN

2024· article· id· W4402283052 on OpenAlexaboutno aff
Retno Fuji Oktaviani, Hakam Ali Niazi, Andri Medina, Nanang Suryadi

Bibliographic record

VenueEquilibrium Jurnal Ilmiah Ekonomi Manajemen dan Akuntansi · 2024
Typearticle
Languageid
FieldSocial Sciences
TopicIndonesian Election Politics and Participation
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness administrationBusiness

Abstract

fetched live from OpenAlex

ABSTRAKKampanye politik di berbagai negara telah mengalami transformasi signifikan dalam beberapa dekade terakhir, terutama dengan kemajuan teknologi dan perubahan perilaku pemilih. Tujuan penelitian ini adalah untuk menemukan kebaruan dalam inovasi strategi kampanye yang dilakukan oleh Presiden diberbagai negara dengan menggunakan database scopus. Metode yang digunakan adalah Metode bibliografi yaitu pendekatan yang digunakan untuk mengumpulkan, menganalisis, dan menyusun daftar referensi atau sumber-sumber informasi yang relevan dengan topik penelitian tertentu. Dalam menelusuri dokumen yang relevan peneliti mencoba memasukkan keyword utama yaitu “politics” dan “campaign” dan “president” dan “strategy” sehingga menghasilkan 117 dokumen dengan rentan waktu tahun 1948 sampai tahun 2025. Bidang pencarian dibatasi pada “judul”, “abstrak”, dan “kata kunci”. Selanjutnya data pengumpulan tidak dibatasi hanya pada artikel ilmiah tetapi juga pada buku, book chapter, review dan conference paper. Hasil Penelitian ini mengungkapkan beberapa temuan penting terkait dengan tren penelitian strategi kampanye politik presiden. Pertama, analisis tren menunjukkan bahwa penelitian mengenai strategi kampanye politik presiden telah mengalami peningkatan yang signifikan selama bertahun-tahun, mencerminkan minat yang terus berkembang dalam bidang ini seiring dengan semakin kompleksnya dinamika politik dan kemajuan teknologi. Kedua, hasil penelitian menunjukkan bahwa negara-negara seperti Amerika Serikat, Inggris, dan Canada merupakan pemimpin dalam jumlah publikasi terkait strategi kampanye politik presiden, dengan penulis dan institusi dari negara-negara tersebut yang sering kali dikutip paling banyak, menandakan kontribusi mereka yang signifikan terhadap literatur global di bidang ini.Kata Kunci: Strategi Inovasi; Kampanye; Politik; Blue Ocean StartegyABSTRACTThe aim of this research is to find novelty in campaign strategy innovations carried out by the President in various countries using the Scopus database. The method used is the bibliographic method, which is an approach used to collect, analyze and compile a list of references or sources of information that are relevant to a particular research topic. In searching for relevant documents, researchers tried to enter the main keywords, namely "politics" and "campaign" and "president" and "strategy", resulting in 117 documents with a time range of 1948 to 2025. The search field was limited to "title", "abstract" , and “keywords”. Furthermore, data collection is not limited to scientific articles but also books, book chapters, reviews and conference papers. The results of this research reveal several important findings related to research trends in presidential political campaign strategies. First, trend analysis shows that research on presidential political campaign strategies has experienced a significant increase over the years, reflecting the growing interest in this field as political dynamics have become more complex and technology has advanced. Second, the research results show that countries such as the United States, the United Kingdom, and Canada are leaders in the number of publications related to presidential political campaign strategies, with authors and institutions from these countries often cited the most, indicating their significant contribution to global literature in this field. Keywords: Innovation Strategy; Campaign; Political; Blue Ocean Strategy

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.313
Teacher spread0.281 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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