Bibliographic record
Abstract
Since February 2016, the financial sector of Azerbaijan, due to the emergence of a single mega-regulator, began to work in a new reality.This event was controversial, and discussions were held on the creation of a mega-regulator.Moreover, practically from the very first steps of its existence, this institution faced unexpected challenges associated with a new wave of the economic crisis and price instability of the global energy market.In the current conditions, an in-depth analysis of mega-regulation is of particular relevance.The modern period is characterized by a dynamic change in the financial markets.In the context of globalization, any imbalance in any sector of this market can cause unforeseen difficulties, including economic collapse.In this regard, financial markets feel an urgent need for an effective model of regulation and supervision.In many states, this leads to active reforms in the respective systems.In world practice, depending on the goals and objects of regulation, there are four models of integration of financial regulation and supervision: Consolidated model: one mega-regulator; full integration, i.e. the state gives one body the powers of microprudential regulation and supervision of all types of financial institutions and financial markets (Canada, Germany, Denmark, Russia, Azerbaijan). "Twin peaks" model: two bodies with different goals; partial sectoral integration, i.e. each body is responsible for at least two types
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".