Publicidad de marca en el entorno digital caso : colombiana 100%
Bibliographic record
Abstract
Actualmente la publicidad digital está marcando tendencia en el mundo. Las redes sociales son esenciales para la comunicación de marca ya que permiten a las empresas tener una comunicación directa con el consumidor de manera rápida. El siguiente trabajo se realizó con el objetivo de analizar el uso de los medios digitales en la publicidad de la marca Colombina 100%. Se encontró que la marca tiene mayor actividad en la red social Facebook que en la red social Instagram lo cual genera mayor interacción con sus usuarios. Se concluyó que la estrategia de Colombina 100% es enseñar a sus consumidores las diferentes formas de consumir el producto. Para realizarlo, presentan videos de recetas e imágenes con el producto que tienen un formato estandarizado para dar a entender al consumidor que Colombina 100% es una marca natural y saludable.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".