The Establishment of a Global Authority and a Single Framework for the Financial Products: Labour Policies and Economic Challenges
Bibliographic record
Abstract
Free markets (Laissez-faire) do not necessarily optimize the balance between work and the level of income and consumption. In incompletely informed markets, active labor policies can improve the balance between the unemployed and job vacancies, as well as income disruptions from uninsured work, increasing employment and production. This, in turn, increases the income of factors that are complementary to work: in fact, where work is the most important source of income for most individuals, however with political decisions, less weight may also be given to other income factors. This is especially true in societies with high wealth inequality, where active policies have less political support than passive policies, which increase the lack of earnings per unit of labor against other factors of production, or balance higher income from work with low productivity and non-working income. Consequently, in a unified and regulated framework, as proposed, and in which the money markets mainly tend towards perfection, "passive" labour policies tend to prevail, especially in societies where there is a higher than average, social wealth, but also less inequality of income from work. Otherwise, if there are inequalities in workers' incomes, in these markets, we need more "active" policies, which will support the production of income by other factors complementary to labour.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.010 |
| 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".