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Record W4413067770 · doi:10.51622/jispol.v5i1.2838

Analisis Faktor-Faktor Yang Mempengaruhi Minat Berwirausaha Pada Masyarakat Di Era Digital Di Kabupaten Nias Selatan

2025· article· id· W4413067770 on OpenAlexaff
Ardima Laia, Maswida Laia, Epaproditus Dachi

Bibliographic record

VenueJurnal Ilmu Sosial Dan Politik · 2025
Typearticle
Languageid
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt

Abstract

fetched live from OpenAlex

Perkembangan teknologi yang masif telah mengantar kita pada era digital, sebuah periode di mana informasi dan konektivitas menjadi tulang punggung kehidupan, mengubah lanskap ekonomi dan kesempatan kerja bagi Masyarakat di Nias Selatan yang kini melihat kewirausahaan sebagai jalur karier menjanjikan di tengah ketatnya persaingan profesional tradisional. Fenomena ini memicu kebutuhan mendalam untuk memahami apa yang mendorong atau menghambat minat Masyarakat dalam terjun ke dunia bisnis di tengah gelombang transformasi digital. Berdasarkan analisis mendalam terhadap persepsi dan pengalaman Masyarakat di Nias Selatan, dapat disimpulkan bahwa minat berwirausaha pada Masyarakat di era digital merupakan konstruk kompleks yang dibentuk oleh interaksi dinamis antara faktor-faktor internal dari diri Masyarakat dan faktor-faktor eksternal dari lingkungannya. Era digital secara signifikan telah membuka babak baru dalam lanskap kewirausahaan, menyajikan peluang sekaligus tantangan unik yang memengaruhi keputusan Masyarakat. penelitian ini menemukan bahwa: peluang digital seperti akses pasar yang luas, modal awal yang relatif rendah, fleksibilitas waktu, dan kemudahan belajar melalui sumber daya online, secara universal diakui sebagai daya tarik utama yang memicu minat berwirausaha. Namun, Masyarakat juga menyadari tantangan seperti persaingan ketat, isu kepercayaan konsumen, dan kebutuhan literasi digital yang berkelanjutan.

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, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0040.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.275
Teacher spread0.260 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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