Comunicar afectivamente: Un marco para el estudio de los afectos en la comunicación visual
Bibliographic record
Abstract
espanolEl objetivo del presente ensayo es esbozar un marco teorico para el estudio de los afectos en la comunicacion visual, particularmente, en las obras de arte cuyo fundamento es la visualidad, a traves del trabajo del filosofo canadiense Brian Massumi. En el estudio de la comunicacion visual se han seguido las pautas de la relacion entre lenguaje y representacion para analizar las formas objetivadas de la cultura, siguiendo la tesis de que el lenguaje es el principio organizador de toda objetivacion cultural. Pero en anos recientes, un viraje epistemologico sostiene que los afectos y las emociones son el motor de la creacion cultural, incluido el lenguaje y sus diversas formas de objetivacion. En este cambio, el estudio de los fenomenos visuales no se centra en el producto, sino en el proceso de creacion donde se encarnan afectos, fenomenos conformados por afecciones corporales y procesos mentales. Esto conduce a considerar a la comunicacion visual como un proceso en transformacion. Sus materialidades capturan el afecto, el cual entra en un progreso semantico que conduce a la formacion de emociones, entendidas como conceptos. EnglishThe objective of this essay is to outline a theoretical framework for the study of affects in visual commmunication, specifically, in artworks which foundation is visuality, through the work of Canadian philosopher Brian Massumi. Within the study of visual communication, we have followed the guidelines that form the relation between language and representation, in order to analyze the objectified forms of culture. In recent years, an epistemological turn argues that affects and emotions are the engine of every cultural creation, including language and its divers ways of objectification, where we find visual communication. In this change, the study of visual phenomena does not centers around the products but creation processes in which affects are embodied. We understand affects as phenomena conformed by corporal affections and mental processes. This leads to consider visual communication as a process subjected to becoming and there, its materialities capture affect, which enters in a semantic progression, leading up to the formation of emotions, understood as concepts.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".