Los efectos de la pandemia por COVID-19 en el comercio internacional
Bibliographic record
Abstract
COVID-19 is one of the greatest events of the last century due to the effects it has generated and the way it has shown the vulnerability of the entire world to various situations; international trade has been severely hit due to restrictions imposed to control the disease.During this article, the effects and behavior of the trade balance before and during this period will be analyzed, based on three different scenarios: Mexico, Latin America and the world, through the analysis of statistical data on the volume of exports, imports and international trade for know the level of the repercussions caused and understand the behavior ofnations in the face of this situation, analyze their strengths, weaknesses, degree of resilience and recovery,through a double-entry comparative table that allows visualizing the differences and similarities of the three scenarios analyzed. The results obtained show that according to the WTO, during the second quarter of 2020 there was a 15.5% decrease in the volume of world trade, while Latin America registered a 9% drop in exports during 2020; Finally, INEGI reported that thegreatest effects caused in Mexico correspond to the month of May 2021, where a deficit in the trade balance of 3.902 million dollars was registered as a result of decreases of up to 50% in exports from the manufacturing sector.As of the last period of 2020 and the first of 2021, a recovery began to be seen in most of the nations of the world, although the speed of this recovery depends on the internal situations and the relations of the countries opposite, so it is necessary have policies that allow supporting exports and reducing the vulnerability of foreign trade.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".