ANALYSIS OF DIFFERENCES IN FINANCIAL PRODUCTIVITY BETWEEN GOVERNMENT BANKS AND PRIVATE BANKS USING VALUE ADDED METHOD
Bibliographic record
Abstract
Abstraksi Tujuan Penelitian ini untuk memperoleh bukti empiris perbedaan Produktivitas keuangan perbankan yang terdaftar di Bursa Efek Indonesia dengan metode Economic Value Aded. Sampel dalam penelitian ini adalah 4 perbankan pemerintah dan 4 perbankan swasta yang terdaftar di Bursa Efek Indonesia periode 2015 sampai 2018. Teknik Pengambilan sampel dengan metode Purposive Sampling yaitu bank swasta yang meberikan deviden setiap tahun dan perbankan swasta devisa lokal. TeknikAnalisis data terdiri dari pengujian normalitas dan Uji beda Paired Sample Test Hasil Penelitian membuktikan bahwa berdasarkan Uji Beda Paired Sample Test dengan metoda EVA didapat hasil bahwa tidak ada perbedaan yang signifikan antara perbankan pemerintah dengan Swasta dalam penilaian Value Added Kata Kunci : Produktivitas Keuangan, Economic Value Adedd
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".