Global Economic Outlook: Scenario Analysis for 2023 and Tendencies
Bibliographic record
Abstract
The purpose of this article was to map the most likely scenario for the year 2023, given the economic prospects in the world, and to project tendencies. To this end, a documental analysis was carried out based on studies made available by organizations and institutes specialized in global economic analysis. The applied method was the exploratory and the technical procedure was the bibliographic and descriptive, using a quali-quantitative technique to analyse and qualify the data. To operationalize the research, the material obtained was manipulated for subsequent tabulation and construction of graphics and tables using the Microsoft Excel application. Among the main elements used to identify the economic impacts, we can quote: (i) the COVID-19 pandemic, (ii) the main tendencies of the economy in the post-pandemic world, (iii) the economic policies adopted by countries in combating to the crisis, and (iv) the behaviour of wage dynamics in this context. The results showed that the economic crisis aggravated by the COVID-19 pandemic caused a global economic recession, severely affecting the cost of living. In the field of tendencies, there was the expectation of low economic growth and restrictive policies that are causing an increase in interest rates. In the labour market, the outlook is for a low supply of workforce with repercussions on wage dynamics, which should remain below the values practiced before the health crisis. The possible solution will depend on the willingness of the countries to promote cooperation to provide priority assistance to the most vulnerable classes and the ability to stabilize prices, avoiding greater pressure on inflation and interest rates, with a direct impact on wages.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".