Bibliographic record
Abstract
International economic conditions are projected to have a favorable path in 2018. Thus, accelerating investment in infrastructure and real estate in China, as well as expectations of fiscal loosening in the United States, lead to an increased expectation for enhancement of global trade flows and to strengthen investor confidence. Instead, Britain's decision to leave the European Union, as well as political uncertainty in some euro-zone countries, may cause temporary distortions but also implications for economies in the european region. On the other hand, the divergence of the Federal Reserve and the European Central Bank's monetary policies, as the US expects further increases in interest rates, can be reflected in the activity of the government bond market as a result of the reorientation of investors to assets with higher yield, a trend amplified by the context of the economic environment with low interest rates. Recent developments in the field of financial technology innovation are an important challenge for conventional financial market (payment and settlement) infrastructures, especially in the context of multiple public and private initiatives and projects developed over the last few years. The digitization of financial services is an international concern due to the complexity of this phenomenon, and the lack of harmonized regulations and / or standards in the field. On the one hand, technological innovation in the field of payment systems has the potential to create a number of social benefits by improving access to financial services (financial inclusion). On the other hand, the integration of new technologies in the financial and banking field may imply additional information security risks, especially on payment and settlement systems. Financial technology innovation projects have also been developed by central banks in Europe, but also in America and Asia (for example in the UK, the Netherlands, Sweden, Canada, China and India). They target digital coin concepts, use in distributed real-time payment systems of blockchain technologology, artificial intelligence, and information security. However, a large number of economists admit that the current crisis has emphasized the overcoming of the limits of (mainstream economics) and its implications in the field of financial regulation and the sphere of monetary policy that should focus not only on price stability, but also on financial stability. In other words, the banks, the financial institutions must be obliged to hold a minimal countercyclical capital and mandatory reserves that should be large enough to cope with the turmoil generated by the decrease of the asset prices and the liquidity risk. In macro-prudential policy, the “shadow banking system” (investment banks, hedge funds, private equity funds) must also be included.Last but not least, the central banks have to cooperate with the other categories of systemic risk managers: Financial Stability Oversight Council-FSOC (USA), European Systemic Risk Board-ESRB (EU), Financial Policy Committee-FPC (Great Britain), National Committee for Financial Stability-CNSF (Romania).
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".