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Record W2581757638 · doi:10.21098/bemp.v6i2.326

APA, BAGAIMANA, DAN DAMPAK REKSA DANA

2004· article· id· W2581757638 on OpenAlex
Kiki Nindya Asih, Wahyu Ario Pratomo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBulletin of Monetary Economics and Banking · 2004
Typearticle
Languageid
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Produk reksa dana (mutual fund) akhir-akhir ini mendapat cukup banyak perhatian dengan perkembangannya yang fenomenal khususnya dalam 12 bulan terakhir2 . Perkembangan fenomenal ini tercermin dari peningkatan jumlah nasabah (pemegang unit penyertaan reksa dana), dana kelolaan, dan produk reksa dana yang beredar di pasar3 . Sampai dengan bulan Desember 2002, jumlah account investor reksa dana tercatat sebesar 125.820 account (143,2%, y-o-y), sementara dana kelolaan secara tahunan melonjak 252,8% menjadi Rp56,1 triliun. Rasio dana kelolaan terhadap DPK meningkat tajam, dari hanya 2% di awal tahun 2002 menjadi 7% pada bulan Desember 20024 . Sementara itu, dari 4 jenis reksa dana yang ditawarkan ke pasar5 , saat ini terdapat 131 macam produk dari hanya 25 macam produk pada awal reksa dana diperkenalkan di tahun 1996.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.159
Teacher spread0.151 · 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