Bibliographic record
Abstract
Context: The COVID-19 pandemic caused a drop in real world GDP in 2020 to -3.5%, mainly due to the decrease in production and international trade.In Peru, the impact of COVID-19 was marked by a drop in exports in general, being theexports of mining products which fell by more than 200%.Purpose:This study analyzes the impact of COVID-19 on mining exports during the period of sanitary isolation by COVID-19. Method: The empirical strategy of the study is based on the use of an econometric model of Ordinary least squares (OLS)with time series.Results:The results of the investigation indicate that mining exports were negatively affected during the period of sanitary isolation by the COVID-19 pandemic.However, the measure given by the Peruvian government on June 30, 2020, through Supreme Decree No. 117-2020-PCM, to resume all mining activitiescaused a positive effect on mining exports.Likewise, this study finds thatcopper and gold exports, as well as the percentage change in China's GDP play a significant role in the performance of Peru's mining exports.However, both the international price of copper and gold were not significant at 95%.Conclusion:The findings of this study imply that the government should promote the responsible production of these minerals, always with a responsible social commitment to care for the environment; and with a look to the future, the development of copper and gold industrialization should be promoted in order to advance towards the levels of a developed economy.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".