Barrio por barrio: reclamando nuestras ciudades / Neighborhood by Neighborhood: Reclaiming Our Cities.
Bibliographic record
Abstract
ResumenMuchos urbanistas siguen planificando las ciudades al margen de sus habitantes. Este texto plantea la necesidad de recuperar el protagonismo de las personas y de su “derecho a la ciudad”, centrando los esfuerzos en la mejora de los espacios en que desarrollan su experiencia cotidiana: los barrios. Para ello primero se analizan las propiedades que caracterizan a un barrio y posteriormente se plantea un posible plan de intervención, que debe comenzar con la asunción, por parte principalmente de las autoridades competentes, de las necesidades de las personas como eje principal de la planificación urbana. En todo el proceso debe contarse con la participación de la gente, identificando y delimitando sus propios barrios y poniendo sobre la mesa tanto sus necesidades y prioridades como sus recursos y capacidades para colaborar en un proceso gradual de mejora.Palabras clave: Plan de Barrio, planificación participativa, ciudad social.AbstractMany planners and architects continue to plan cities without taking their inhabitants into account. This text dwells on the need to recover the role of people and their “right to the city” and focuses on efforts to improve the spaces in which citizens can develop their day-to-day experiences (neighbourhoods). To do this, it is necessary to first analyse the properties of the neighborhoods in question and then establish an intervention plan, which must start off with the assumption, by the most important competent authorities, of the needs of citizens as the basic line of town planning. Citizens must take part in the entire process, identifying and delimiting their own neighborhoods and explaining their needs and priorities, and their resources and capacity to collaborate in a gradual process of improvement.Keywords: Neighborhood Plan, Participatory Planning, Social City.
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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 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".