Bibliographic record
Abstract
Purpose The purpose of this paper is to encourage critics and artists to make use of a cybersystemic perspective in their work to improve its potency and long‐term value to humankind and the larger living world. The arts are centrally involved in the competitive propagation of our deep cultural identities and involved also in the marketing needed to ensure our biological identity propagation. We need better ways to formatively evaluate the arts so that requisite life‐enhancing control variety can be universally available. Unfortunately, the arts are not widely enough understood to be the crucial system steering activities that they are, for us to realize the immense visionary guiding benefits they can offer for solving the very serious global problems of the twenty‐first century. Design/methodology/approach This is a conceptual paper that proposes a methodology to enable critics and artists to make use of a cybersystemic perspective in their work. Findings Transformative re‐education of artists and critics to develop cybersystemic leveraging of their own work is now possible by deploying via the web: systemic modeling, simulations, and educative dramatic role‐play games together with learning conversations. The essential content in education for human long‐term viability has to do with how complex system steering really works and precisely how the arts play such a central role in it all. Originality/value Education which specifically demonstrates how cybersystemic viability principles such as: good closings, balancing loops, requisite variety, requisite heterarchy, and multi‐level learning conversations work can be used by artists and critics to steer human activity better and so can be a big part of the solution to the severe threats that the world is now experiencing.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.084 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".