Naturaleza Privada y Calidad de Vida. Influencia de la naturaleza doméstica en el bienestar de los propietarios de casas con patio de la ciudad de Córdoba, España.
Bibliographic record
Abstract
Resumen \n \nNumerosas investigaciones consideran que los espacios verdes públicos de las ciudades son lugares privilegiados para que se desarrollen los procesos de socialización e interacción entre los ciudadanos y la naturaleza. Sin embargo, este trabajo demuestra que los ciudadanos que viven en determinados espacios privados, como las casas con patio del barrio histórico de Córdoba, pueden llegar a percibir tantos o más beneficios sociales en sus patios, que si visitaran otros espacios verdes de la ciudad. Para desarrollar este trabajo se utilizó una metodología cualitativa; utilizando la técnica de los grupos de discusión y una metodología cuantitativa; mediante la realización de una encuesta presencial. La investigación muestra que los patios de la ciudad de Córdoba son los espacios verdes mayormente escogidos por sus propietarios para pasar su tiempo libre, y que el contacto y cuidado de estos pequeños espacios, contribuyen a generar en sus propietarios y usuarios una satisfacción y nivel de bienestar igual o incluso superior al provocado si visitaran otros espacios verdes de la ciudad. \n \n Palabras Clave: Naturaleza privada; Calidad de vida; Patios; Naturaleza Urbana; Espacios Verdes Urbanos \n \n \n \n \nAbstract \n \nNumerous studies consider public green spaces in cities are places to development processes of socialization and interaction between people and nature. However, this work shows that people living in certain private areas, such as courtyard houses of the historic quarter of Cordoba, can receive as many or more benefits in their courtyards, that if they visit other green spaces in the city. To develop this work we used a qualitative methodology, using the technique of focus groups, and quantitative methodology, by conducting a face to face survey. Research shows that the courts of the city of Cordoba are mostly green space chosen by the owners to spend their free time, and that contact and care of these small spaces, all contribute to its owners and users level of satisfaction and being equal to or higher than the effect even if they visited other urban green spaces. \n \nKey Word: Private Nature; Quality of Life; Courtyards; Urban Nature; Urban Green Spaces
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".