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Record W4233716172 · doi:10.31227/osf.io/rst7q

Gerakan Buru Membaca sebagai Media Pembelajaran Masyarakat di Kabupaten Buru

2018· preprint· id· W4233716172 on OpenAlexaff
Pusat Studi Perencanaan dan Penelitian Uniqbu, M Yusran Zakaria, Salma Yusuf, S. Rachman, Wa Malmia, Belinda Sam, Abdul Latif Wabula, Said Abdurahman Assagaf, Irma Magfirah, Iskandar Hamid, Siami Prafitriyani, Lutfi Rumkel, Muhammad Bula, EDI SAID NINGKEULA SP, Riki Bugis, Mirja Ohoibor, Sukainap Pulhehe, Mansyur Nawawi, Hamiru, Rosita Umanailo, Idrus Hentihu, Abdi Wael

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsGeographySociologyHumanitiesArt

Abstract

fetched live from OpenAlex

Tujuan penelitian ini mendapatkan gambaran primer tentang media pembelajaran masyarakat yang terbangun melalui gerakan buru membaca di Kecamatan Namlea. Pendekatan yang dipergunakan dalam penelitian ini menggunakan pendekatan kualitatif dan Jumlah informan yang akan diwawancarai sebanyak 30 orang yang diambil secara purposive dengan pertimbangan responden dianggap sebagai pihak-pihak yang terkait untuk mencapai tujuan penelitian. Penelitian dilakukan di Kecamatan Namlea Kabupaten Buru dengan sampel wilayah adalah Desa Namlea, Desa Jamilu dan Desa Lala. Penelitian ini menggunakan teknik pengumpulan data atau teknik yang menggunakan observasi, wawancara mendalam untuk mendapatkan data kondisi kondisi sosial budaya masyarakat, terkait pelaksanaan gerakan buruo membaca. Teknik analisis yang digunakan dalam penelitian ini adalah analisis data kualitatif mengikuti konsep yang diberikan Miles and Huberman dan Spradley.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.002
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0360.008

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 head0.062
GPT teacher head0.329
Teacher spread0.268 · 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 designQualitative
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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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