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Record W3137360760 · doi:10.31219/osf.io/7j6wz

Akses dan Penggunaan TIK pada Rumah Tangga dan Individu di Kecamatan Barru

2020· preprint· id· W3137360760 on OpenAlexaff
Rahmita Saleh

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPolitical scienceBusiness administrationHumanitiesBusinessPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Bagi negara berkembang seperti Indonesia, mengukur akses dan penggunaan TIK adalah kunci untuk memantau kemajuan negara menuju masyarakat informasi dan merupakan bagian penting untuk analisis dan perencanaan kebijakan bidang TIK dan kebijakan terkait lainnya. Dalam kaitannya dengan perancangan kebijakan, penting untuk melakukan pengukuran TIK di Kecamatan Barru Kabupaten Barru karena masuk dalam Kawasan Perdesaan Prioritas Nasional (KPPN), sebuah kawasan perdesaan yang ditetapkan oleh Bappenas dan Kemenko PMK guna mengurangi kesenjangan antara desa dan kota dalam berbagai sektor.Dalam agenda kebijakan KPPN, 4 dari 5 Desa di Kecamatan Barru termasuk didalamnya. Pengukuran ini dilakukan dengan tujuan untuk mendapatkan data statistik yang komprehensif dan sebanding untuk mendukung keputusan kebijakan pemerintah dan industri dalam bidang TIK untuk mengurangi kesenjangan digital di wilayah tersebut. Metode yang digunakan adalah survey terhadap 384 responden. Teknik pengumpulan data dilakukan melalui wawancara tatap muka dengan menggunakan kuesoiner. Indikator yang digunakan mengacupada International Telecommunication Union (ITU) pada tingkat rumah tangga dan individu. Hasil pengukuran menunjukkan bahwa akses dan penggunaan terhadap telepon seluler dan internet sangat tinggi di Kecamatan Barru, namun akses dan penggunaan komputer masih rendah.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.775
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.041
GPT teacher head0.282
Teacher spread0.241 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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