Comparación de la inflación-desempleo de Estados Unidos y México al inicio de la pandemia de COVID-19
Bibliographic record
Abstract
As of the first quarter of 2020, economies around the world began a slowdown in their activity due to the confinement generated by the COVID-19 pandemic. In several economies, expansionary monetary and fiscal policies were implemented to try to avoid a drastic drop in unemployment and production. The objective of the work is to analyze the relationship between the levels of inflation and unemployment at the beginning of the pandemic, both in Mexico and in the United States of America, and to assess whether there is a difference in said relationship between both countries. Monthly inflation and unemployment data were used from January 2016 to December 2020, considering a Generalized Additive Model (MAG) to study the relationship between inflation and unemployment in both countries. The expected results would suggest the evident existence of the Phillips curve in the short term in the United States, but not in Mexico, so it would not justify an expansive fiscal policy in the latter to stimulate the economy. It is concluded that orthodox economic policies to stimulate the economy and reduce unemployment would not necessarily have the expected effects in the case of Mexico but would have an increase in prices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".