Análisis del perfil demográfico y consumo turístico en eventos deportivos en la ciudad de Quito. Caso de estudio: Roger Federer (Analysis of the demographic profile and tourist consumption in sporting events in the city of Quito. Case of study: Roger Fed
Bibliographic record
Abstract
El desarrollo del turismo en las ciudades plantea dos aspectos de importancia. En primer lugar, es su objetivo que el turismo impacte en el desarrollo local y, en segundo lugar, que la oferta turística se diversifique y se complemente. En esta coyuntura, la ciudad de Quito apostó por el apoyo a un evento deportivo, de gran envergadura, para el mes de noviembre del 2019. De esta propuesta surgió la necesidad de medir el impacto del evento deportivo en la ciudad y al mismo tiempo identificar la demanda real de turistas nacionales e internacionales. Para ello se aplicó un cuestionario con 23 ítems que fueron agrupados en 2 grupos de factores: la determinación del perfil del consumidor y el consumo turístico realizado. Posteriormente se obtuvieron datos de Quito Turismo sobre el media value del evento, con el cual se estructuró una base de datos para una interpretación integral. Los resultados obtenidos fortalecen la estrategia para que Quito albergue varios eventos de importancia, en diversos aspectos, a más de lo deportivo. La derrama económica en los negocios locales fue significativa y se registró un impacto importante en el posicionamiento de la ciudad como destino turístico. Abstract. The development of urban tourism raises two important aspects. In the first place, tourism must have an impact on local development and, secondly, that the tourism offer should be diversified and complemented. At this juncture, the city of Quito opted to support a large-scale sporting event for the month of November 2019. From this proposal arose the need to measure the impact of the sporting event in the city and at the same time identify the real demand of national and international tourists. For this, surveys were conducted with 23 items that were grouped into 2 groups of factors: the determination of the consumer profile and the tourist economic consumption. Afterwards, data was obtained from Quito Tourism on the average media value of the event, with which a database was structured for a comprehensive interpretation. The results obtained strengthen the strategy for Quito to host several important events, in various aspects, in addition to sports. The economic impact on local businesses was significant and there was an important impact on the positioning of the city as a tourist destination.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".