Migrant Tools: Recursos de Aprendizaje para un Mundo de Entidades en Tránsito
Bibliographic record
Abstract
Esta comunicacion relata el contexto de aprendizaje de ejercicios compartidos por estudios en humanidades, ciencias sociales y arquitectura en 2018-2019, los cuales exploran metodologias a priori exclusivas pero con una cierta capacidad de vehicular conversaciones y debates. El punto de partida han sido los relatos llamados “Journeys. How travelling fruit, ideas and buildings rearrange our environment” editados por la Canadian Centre for Architecture (Borasi, 2010). El contenido y sus formas de escritura han favorecido maneras abiertas de encarar estudios de sociologia urbana y la emergencia de nuevos programas de proyectos arquitectonicos. La primera parte de la comunicacion explica el modo de interpretar la arquitectura y como esta va evolucionando en clave de tipologias adaptativas, reforzado por estudios de movilidad realizados desde las ciencias sociales y humanidades que ponen el foco tanto en cuestiones locales y regionalismos como en fenomenos de transculturalidad y globalizacion. Por ejemplo, en Guggenheim y Soderstrom (2009) se da valor a la movilidad de ideas, tipos, imagenes y materiales y su relacion con la forma de ir mutando arquitectura, cultura y ciudad. La comunicacion termina explicando dos de las dinamicas docentes y tres de los casos de estudio, postproducidos por los docentes integrantes de la red Viceversos usando flujogramas situacionales y un interface digital llamado “Migrant Matters” que incluye todos los materiales producidos.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".