TOWARDS A METHODOLOGY FOR INTERPRETING VISUAL IMAGES AS CYBERNETIC SYSTEM: LACAN AND DELEUZE
Bibliographic record
Abstract
This exposition endeavors to outline a theoretical framework for a methodology to interpret visual images that draws on cybernetics, semiotics, psychoanalysis and philosophical ideas. Using images, aesthetics and artistic practices as a means of generating new understanding requires translating, deciphering and interpreting those artistic products and/or processes. How can one decipher the system of visual language that underlies artistic productions? I suggest that cybernetics is requisite for such an endeavor. Cybernetic theory is the science of relations within a system, taking as its problematic the relation between a system and its productions or output; in some instances, it studies how the productions of a system influence the system itself. This exposition endeavors to articulate aesthetics or artistic works in terms of a visual language and as a cybernetic enterprise in the context of art-based research by drawing on the ideas of Lacan and Deleuze. For Lacan, aesthetics exists as a primary mode of discourse for the articulations of the unconscious, as evidenced in images in dreams, art and fantasy. Lacan is renowned for his dictum that the unconscious and its productions are structured like a language, but the kind of structure of meaning at work in the unconscious is less related to the structural grammar of a natural language than the syntax of mathematics and cybernetics. Drawing on Lacanian dream analysis, I evince how such an approach could be applied to aesthetic phenomena. Deleuze presents a semiotic theory, a theory of signs which evinces the generation of novel meaning in the unconscious; it can be said to be cybernetic in the way that it exists in a state of continual evolution, the output produced by the system engendering transformation in the system itself. Deleuze offers a framework for how the work of art or aesthetic phenomenon can be translated into new knowledge through the process of entrainment with signs.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.040 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| 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".