Planning and designing urban open spaces for low income neighbourhoods in Chile : case study, Alto Hospicio Chile
Bibliographic record
Abstract
With the global increase in the density of urban population, policy makers and planners have been paying significantly more attention to measures designed to promote sustainable development and to improve the quality of life in the urban environment. Chile’s marked demographic explosion and its rapid urbanisation increased the housing demand; as a result, this overcrowding created land invasions and informal settlements. From the 1980s to 2006, the Chilean government implemented a policy that reduced the total housing deficit by half, unfortunately, this policy favoured quantity over quality and resulted in extended social housing complexes as opposed to designing complete neighbourhoods. In addition, unplanned and informal settlements arose in many regions of the country and were relocated to the periphery of existing cities. This excluded residents from their entire social and economic system. Due to a lack of spatial and social connections, especially urban open space, these communities have morphed into pockets of inequity, delinquency and spatial segregation. Using a case study approach to address the research questions, this study evaluates how urban open space currently functions in Alto Hospicio and aims to contribute with a design framework that may guide and inform government, municipal authorities, planners, and designers in the implementation of more adequate urban open spaces in the under-utilized landscape of Chile’s low-income communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".