Determinantes del desempeño empresarial en el Ecuador durante la pandemia de COVID-19
Bibliographic record
Abstract
The COVID-19 crisis hit the national economy in a negative and unexpected way because, in an attempt to preserve the health of Ecuadorians, the Government took preventive actions to avoid contagion of this disease, such as: suspension of economic activities and the confinement of the first months (March and April 2020) of the crisis. This caused a decrease in the country's domestic demand, since in the second quarter of 2020 there was a decrease of 12% compared to the previous quarter, which undoubtedly affected the supply sector since it contracted by approximately 13% ( Central Bank of Ecuador, 2020b), including in the latter, businesses and / or enterprises of the different sectors of the country's economy and weakening the business apparatus of Ecuador. \nDespite the fact that in the first months of pandemics (March, April, May) there were high unemployment rates and little creation of new companies (ECB, 2020a; Superintendency of Companies, 2021), these figures have improved inferring that the business apparatus of the economy is in the process of reactivation. In this way, the objective of this research is to identify, based on the pre-existing literary review, the factors that influence the performance of companies in this context of crisis, to contribute to making the right decisions in the business sector of Ecuador.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.002 | 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".