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Record W3210413310 · doi:10.31092/irj.v1i2.9

PERAN PT SARANA MULTIGRIYA FINANSIAL DALAM LIKUIDITAS PEMBIAYAAN PERUMAHAN

2020· article· id· W3210413310 on OpenAlexaff
Rayhan Mahatma Harikusuma, Roby Syaiful Ubed

Bibliographic record

VenueIndonesian Rich Journal · 2020
Typearticle
Languageid
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsWiLAN (Canada)
FundersKementerian Keuangan Republik Indonesia
KeywordsBusinessBusiness administrationAgricultural scienceEnvironmental science

Abstract

fetched live from OpenAlex

Sarana Multigriya Finansial merupakan Badan Usaha Milik Negara yang membantu pemerintah dalam sektor pembangunan pasar pembiayaan sekunder dan primer. Secara garis besar, peran SMF adalah menyediakan dana jangka panjang bagi lembaga penyalur KPR sehingga likuiditas lembaga tersebut dapat terjaga. Untuk memperkuat dan membangun pasar primer dan sekunder maka SMF membantu dengan memberikan sekuritisasi aset keuangan dan pemberian pinjaman. Pada tahun 2018, SMF mendapatkan penugasan pemerintah untuk dapat membantu menyediakan dana pada porsi perbankan pada Fasilitas Likuiditas Pembiayaan Perumahan (FLPP)

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: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.182

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0550.010

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 head0.034
GPT teacher head0.268
Teacher spread0.234 · 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
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

Citations4
Published2020
Admission routes1
Has abstractyes

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