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Record W4386642567 · doi:10.32736/abdimastek.v3i1.1384

Peningkatan Minat Baca Masyarakat Melalui Program Taman Baca Masyarakat “Genangan Ilmu” Di Kecamatan Rangkui Pangkalpinang

2022· article· id· W4386642567 on OpenAlexaff
S Sinta, Maya Saftari, Yuni Iswanto, Marna Marna, Anisah Anisah

Bibliographic record

VenueJurnal Abdimastek · 2022
Typearticle
Languageid
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Membaca adalah jendela dunia dan Buku adalah jendela dunia, dan kegiatan membaca buku merupakan suatu cara untuk membuka jendela tersebut agar kita bisa mengetahui lebih tentang dunia yang belum kita tahu sebelumnya. Kegiatan tersebut dapat dilakukan oleh siapa saja, anak-anak, remaja, dewasa, maupun orang-orang yang telah berusia lanjut. Buku merupakan sumber berbagai informasi yang dapat membuka wawasan kita tentang berbagai hal seperti ilmu pengetahuan, ekonomi, sosial, budaya, politik, maupun aspek-aspek kehidupan lainnya. Selain itu, dengan membaca, dapat membantu mengubah masa depan, serta dapat menambah kecerdasan akal dan pikiran kita.Tanpa kita sadari, manfaat membaca buku dapat memberikan banyak inspirasi bagi kita. Namun sayangnya kegiatan membaca buku akhir-akhir ini telah banyak diabaikan berbagai kalangan dengan alasan kesibukan, maupun karena adanya media yang lebih praktis untuk mendapatkan informasi seperti televisi, radio, maupun media internet. Oleh sebab itu teman-teman Backpacker Teaching Bangka Belitung Mmeberikan solusi dengan mendirikan taman baca masyarakat “Genangan Ilmu” di kecamatan Rangkui Kota Pangkalpinang untuk menumbuhkan minat serta budaya membaca dikalangan masyarakat Kata kunci : minat, baca, taman bacaan

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0630.016

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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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