Interactions – A CyberSystemic Model and an Observation Framework Proposal
Bibliographic record
Abstract
Interactions are the dynamic part of every system, which is elaborated in nearly every domain of human exploration. However, to the best of our knowledge, a general formulation of interactions is not provided. Therefore, a CyberSystemic model of interaction with an observation framework is proposed in this chapter to help in closing this gap. To address the complexity of interactions, a combination of CyberSystemic concepts and methodologies is utilised in a comprehensive interactions model. The chapter proposes an interaction model and an observation framework leading towards an interaction meta-framework, which could be used to generate more holistic observation records, enabling reasoning on a selected interaction performance. The proposed model, invoking related structures and interactions, and a multi-point observation framework, is intended to be applicable to all instances of interactions. Its purpose is to help define and standardise interaction observation data needed for understanding each selected interaction in its environment. It proposes researchers to define complex ontologies on interactions in order to understand the interaction requisitely enough to predict their development and propose measures for reaching interaction goals. The interactions model and observation framework proposal are enabled by artificial intelligence (AI) capacity to augment human limited capacity to store and reason upon large data quantities. By formalising related data models, it enables combining data from multiple sources, enlarging data structures, the number of cases and the overall data quality, all of which affect the level of understanding of the observed interaction. Additionally, due to the standardised observation formalism, building multiple models according to a general meta-framework enables data sharing and identifying patterns across different disciplines.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".