Multi-Player City: la producción de la ciudad negociada: simulaciones docentes
Bibliographic record
Abstract
‘Multi-Player City’ propone una exploración en torno a la idea de ciudad negociada. Su producción se gestiona en países centroeuropeos a través de herramientas como el ‘collaborative planning’, basado en la generación de una mesa de negociación en la que confluyen los diferentes agentes que intervienen en la ciudad para definir un planeamiento en común, negociado y sometido a crítica desde diferentes intereses, necesidades y demandas. El objetivo de esta comunicación es la formulación de una propuesta metodológica centrada en la utilización del juego como herramienta docente para la enseñanza de los valores del planeamiento colaborativo, con el fin de formar a arquitectos en modelos de urbanismo más democrático. La experiencia docente expuesta reproduce el planeamiento colaborativo del ámbito profesional en una simulación académica, transmitiendo al estudiante la desaparición del concepto de autoría, que se traslada del individuo al colectivo, reflejando el cambio del rol del arquitecto en la producción de la ciudad contemporánea. ‘Multi-Player City’ proposes an exploration about the concept of the negotiated city. Its production, developed in Central European countries and based on tools such as ‘collaborative planning’, is founded on the generation of a negotiation table where different agents involved in the production of the city merge. They aim to define a shared negotiated planning subject to scrutiny by different interests, needs and demands. The goal of this communication is the formulation of a methodological proposal based on gaming as a pedagogical tool for teaching the values of collaborative planning in order to train architects on alternative democratic urban models. The exposed teaching experience reproduces collaborative planning methods from the professional field into an academic simulation transferring the student the disappearance of the authorship concept, which moves from the subject to the collectivity, as a reflection of the changing role of the architect in the production of the contemporary city.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".