Back and forth: cybernetics interrelations and how it spread in Latin America
Bibliographic record
Abstract
Cybernetics is a science characterized by the utopian search for new relationships between different areas of knowledge. After the Second World War, the best-known references in Western academia were Norbert Wiener’s approaches to this new discipline. However, there is another little-known hemisphere of this development that remains understudied and we claim is key for its history which refers to the pioneering work of scientists, engineers and cultural practitioners in Latin America, as well as the materialization of specific experiences that lead us to reflect on the role that some regional milestones could have had in the global context. This volume of AI & Society covers points of view that were structured in the various most emblematic stages of these trajectories with the participation of agents that went beyond the assimilation and interpretation of external models, transforming themselves into fundamental and pioneering experiences, among others, the work of Mexican scientist Arturo Rosenblueth, or the impact of the concept of Autopoiesis . Through this article we introduce the outcome of the research—presented in great length in the contributions of this volume—on some of the main stages and trends that constituted the evolution of cybernetics in Latin America. The particular contributions of the authors in this issue have helped to reconstituting these contexts while developing a continuous horizon which also explores future practices.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".