Bibliographic record
Abstract
The current state of the US Federal finance system is analyzed, characterized by record absolute and relative parameters of budget deficits and gross Federal government debt. It is emphasized that the record indicators of debt and deficit objectively testify to the growing vulnerability of the US Federal finance system to external waves and shocks. Such a shock to the American economy was the coronavirus pandemic, the impact of which the United States began to feel acutely in the spring of 2020. The article draws parallels between the current crisis of the Federal finance system and the crisis of the mid-1940s. However, if in the mid-1940s, the colossal size of budget deficits and debt dependence of the United States was a consequence of the previous 15 years of the Great Depression of the 1930s and World War II in the first half of the 1940s, now the record indicators of deficits and debt of the Federal government formed in the last year or two. As a result, the usual process of ranking and establishing a system of budget priorities has been disrupted in the United States covering first of all distribution of budgetary funds between military and civilian expenditures. At the same time, a process of blurring between the parameters of "budget authority" and "budget outlays" began to occur which does not give a clear idea of the exact amount of budgetary funds allocated to the main activities spheres of the Federal government. Nevertheless, we can say that the growing financial constraints have already resulted in the stabilization of US military spending at the level of about $ 700.0 billion annually and the reduction in funding for many key programs for the procurement of the new weapons systems. There is also a growing awareness in the United States of the fact that in modern conditions many civilian programs, especially in the health sector, play no less important role in ensuring the national security of the United States in the broadest sense of the word than the actual military programs. In general, the Democratic administration of J. Biden has not yet succeeded in formulating a clear and coherent fiscal policy, which is carried out in many ways in the "manual control" mode.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".