finans_piyasasi_donusum_dijital_vs_geleneksel
Bibliographic record
Abstract
Series of article: "Finans Piyasasında Dönüşümün Kavgaları: Dijital Bankacılık Geleneksel Bankacılığa Karşı" "Transformation Fights in the Financial Markets: Digital Banking vs Traditional Banking" The institutions that provide our data set are the Banking Regulation and Supervision Agency of Turkey (BDDK) and the Banks Association of Turkey (TBB). BRSA data: Monthly Banking Sector Data - Other Information https://www.bddk.org.tr/BultenAylik TBB data: TBB/Bank and Sector Information/Statistics and Data Inquiry Statistics/Reports/Alternative Distribution Channels/Digital, Internet and Mobile Banking Statistics The data accessed from the TBB data set is 3 monthly data. Taken as digital banking statistics. The data set of the study consists of the number of registered digital banking users(DBK), the number of active digital banking users(DBA), and the average number of branches(OSB) for 59 quarters, including the third quarter of 2020. The DBK and DBA series were used in the study. They were compiled from the DT22 coded report to reflect the total of individual and corporate customers. The data obtained from the BRSA monthly bulletin were converted to quarterly data in accordance with the TBB data set.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.008 |
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.
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".