LA AMIGABILIDAD DE LAS CIUDADES CON LOS ANCIANOS: EL CASO MALMÖ; SUECIA
Bibliographic record
Abstract
En la historia reciente de las ciudades algunos grupos sociales como las personas con alguna inca -pacidad, los niños y los ancianos tienen dificultades para usar sus infraestructuras y sus servicios, es decir, las ciudades no son amigables con ellos; en el caso particular de los ancianos, además de su marginalización, hay que agregarle que su número es creciente y que en el año 2050 serán la cuarta parte del total de la población mundial. El objetivo de este trabajo es desarrollar una metodología que permita cuantificar la amigabilidad de las ciudades con sus adultos mayores, y aplicarla, en la ciudad de Malmö, Suecia. Se identificaron cinco variables en el ambiente físico de las ciudades que determinan su amigabilidad con los ancianos y se evaluaron 27 indicadores en 54 puntos de la ciudad para cuantificar estas variables y obtener su grado de amigabilidad con los ancianos.Recent history of cities shows that some social groups, principally the disabled, children and the elderly, have been marginalized. In large part, these groups still have limited access to city infra -structures and services and they often regard cities as being unfriendly places. With regard to the aged, the numbers of elderly are increasing each year, with seniors expected to represent nearly one-quarter of the planet’s population by the year 2050. This study identified five variables of the physical environment of cities that determine their level of friendliness to the elderly and 27 indicators to quantify these variables. An evaluation of these indicators was performed at 54 refer -ence points of the city of Malmö, Sweden in order to arrive at a classification of the city’s degree of elderly friendliness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".