ESTRATEGIA PARA LA COMERCIALIZACIÓN DEL SERVICIO PREMIUM DEL HOTEL PLAYA PESQUERO - STRATEGY FOR THE MARKETING OF THE PREMIUM SERVICE OF THE PLAYA PESQUERO HOTEL
Bibliographic record
Abstract
La comercializacion en hoteles de lujo es relevante para satisfacer las demandas crecientes de clientes, en ello, los servicios Premium son fundamentales para mantener los ingresos planificados. La investigacion definio como problema cientifico el siguiente: La insuficiente comercializacion desde lo estrategico del servicio Premium, limita los ingresos de este servicio en el hotel Playa Pesquero. Se trazo como objetivo general: Desarrollar una estrategia para la comercializacion del servicio Premium, que contribuya a incrementar los ingresos de este servicio en el hotel Playa Pesquero. Se emplearon varios metodos teoricos como: analisis y sintesis, inductivo-deductivo, sistemico estructural, y entre los empiricos: la observacion cientifica, entrevista, la revision de documentos y el criterio de especialistas. La estrategia para la comercializacion del servicio Premium se estructuro en cuatro componentes esenciales: objetivos, fases, tareas, y tecnicas a emplear. La aplicacion parcial de la estrategia ha logrado entre sus resultados los siguientes: diseno del perfil de mercados meta: Canada, Reino Unido, Alemania e Italia; la actualizacion de los objetivos estrategicos; el diseno de las estrategias de producto, precio, distribucion y promocion; y un plan de accion en funcion de las estrategias trazadas, todo lo cual contribuyo al incremento de los ingresos totales del servicio Premium.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".