The similarity of characteristics between cybernetics and interactivity: How to identify interactive systems/artworks using cybernetic thinking
Bibliographic record
Abstract
Cybernetic theory and interactivity have much in common, including human interrelationships between modern technology and how they define and reveal the whole interactive process. Most of the key notions in both can be described as the system in conversation about the system, talking to each other through the information passed back and forth between the particular relationship in audiences and artworks. These similar languages are feedback, control, conversation and system thinking in the field of cybernetic theory and interactive artworks. As can be seen, some concepts of the cybernetic are applicable to interactivity. So, how can cybernetic thinking be applied to interactive artworks? The purpose of this article is to explore the interplay of cybernetics theory and interactivity and the connection between cybernetic/system thinking and technological/interactive artworks by illustrating the similarity of characteristics and comparing the conversation of two network systems. The goal is to deconstruct and reshape their relationships by thinking of interactive artworks in the way of cybernetic thinking.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.043 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".