Rupiah vs Ringgit: Studi Kasus Penggunaan Dua Mata Uang di Pasar Perbatasan Indonesia-Malaysia
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menggambarkan nilai ringgit dan rupiah dari pandangan masyarakat desa aji kuning dan jenis-jenis komoditas apa saja yang diperjual-belikan pada masyarakat serta bagaimana praktek transaksi jual-beli menggunakan ringgit dan rupiah pada masyarakat Desa Aji Kuning. Jenis dan pendekatan penelitian yang digunakan ialah deskriptif dengan menggunakan metode kualitatif, dan dengan menggunakan teknik pengumpulan data yakni observasi partisipasi, wawancara, dan dokumentasi penelitian. Hasil penelitian bahwa dalam konteks penelitian ini, konsep nilai diterapkan untuk melihat atau membandingkan mata uang ringgit dan rupiah. setidaknya terdapat lima nilai pandangan yang ditemukan yaitu: aspek sejarah, aspek kemudahan akses, aspek komoditi, praktis, aspek keamanan. Serta komoditas yang diperjual belikan seperti barang kebutuhan primer, barang kebutuhan sekunder, barang kebutuhan tersier
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".