Determinantes del gasto en e-commerce debido a la COVID-19: un análisis empírico para los micronegocios en Monterrey, México
Bibliographic record
Abstract
"El objetivo de este artículo es analizar los determinantes que afectan el gasto en que incurren los micronegocios de Monterrey, México, para establecer su modelo de negocio en e-commerce a inicios de la pandemia por la COVID-19. Se emplea una muestra de 661 microempresarios durante el segundo trimestre de 2020. Con la información recabada, se efectúa un modelo Tobit censurado con solución de esquina, el cual permite explicar los factores que influyen en el gasto de una plataforma digital para llevar a cabo el e-commerce de los micronegocios afectados por la pandemia. Los resultados muestran que las características particulares de los propietarios (edad y género) y sus características estructurales (antigüedad, seguridad, clientes y utilidad) impactan significativamente la probabilidad de gastos de adquisición de plataformas digitales, para llevar a cabo el e-commerce de sus productos y servicios."
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".