Viceversos, prácticas docentes compartidas entre Humanidades, Ciencias Sociales y Arquitectura
Bibliographic record
Abstract
Esta memoria relata el contexto de aprendizaje de ejercicios compartidos por estudios en humanidades, ciencias sociales y arquitectura en 2018-2019, los cuales exploran metodologías a priori exclusivas pero con una cierta capacidad de vehicular conversaciones y debates. La estrategia de aproximación, como en otras ocasiones, es la de proponer dinámicas y aprendizajes por experiencia que mezclen integrantes docentes y estudiantes. El punto de partida es la colección de relatos “Journeys. How travelling fruit, ideas and buildings rearrange our environment” editados por la Canadian Centre for Architecture (Borasi, 2010). Los relatos hablan de migraciones forzadas o voluntarias producidas durante las últimas décadas, el impacto que producen y las condiciones que las sustentan. La forma de mostrar las controversias tiene que ver con desplazar la forma habitual del discurso antropocéntrico hacia una realidad ficcionada con la ayuda de agentes tales como cultivos, animales, materiales, climas, y otros no humanos. El trabajo de la red consiste en crear un marco que explore las fronteras comunes entre el pensamiento científico, social y las humanidades en busca de espacios “acreditados” en los que fomentar una visión compartida.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".