Percepción de imagen país. Una aplicación a Nicaragua
Bibliographic record
Abstract
espanolEl presente trabajo tiene como objetivo analizar el concepto de imagen percibida de Nicaragua, sus dimensiones, medicion y principales aplicaciones en el area de Turismo, con el fin de identificar lineas de accion para el desarrollo del sector Turismo. Para ello, en una primera parte se realiza una revision de la literatura academica existente, se analiza el concepto imagen pais percibido desde el punto de vista del Marketing Turistico. En un segundo apartado se examinan las caracteristicas de los turistas que visitaron Nicaragua procedentes de sus principales mercados emisores (Europa, Estados Unidos de America y Canada), asi como la imagen percibida de Nicaragua. Los resultados permiten identificar lineas de accion que se asocien con los planes estrategicos de la administracion publica para el desarrollo del sector turistico, que permitan mejorar la imagen pais y atraer futuros turistas. EnglishThis paper aims to analyze the concept of perceived image of Nicaragua, dimensions, measurement and main applications in the area of tourism, in order to identify lines of action for the development of the tourism sector. Therefore, in a first part a review of existing academic literature is performed, the country image perceived from the point of view of tourism marketing concept is analyzed. In the second section the characteristics of tourists who visited Nicaragua from its main markets (Europe, USA and Canada) as well as the perceived image of Nicaragua are discussed. The results identify courses of action that are associated with the strategic plans of government for the development of the tourism sector to improve the country's image and bring future tourists.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".