Bibliographic record
Abstract
Purpose This is an exploratory analytical paper. It aims to show how systemic learning is explained formally by evolutionary sets and topologies of ordinal values of knowledge‐flows, the knowledge‐induced socio‐scientific variables and the relational mappings in terms of knowledge‐flows and their induced socio‐scientific variables. Design/methodology/approach Mathematical theory of sets and topology is used to study the evolutionary impact of learning on social problems, whereby the impact can be transmitted into sets and topology for measurement. Findings The properties are of interaction, integration and creative evolution of the knowledge‐flows and their knowledge‐induced socio‐scientific variables and relations that are realized by circular causation interrelations. Such systemic learning emanating by circular causation relations is defined by measurable mappings over sets and topologies. Research limitations/implications The cybernetic nature of the paper points toward potential machine interface with cognitive measurement of learning values that are causally linked with social interaction. Originality/value This paper contributes a revolutionary way of measuring consciousness and learning parameters in the framework of evolutionary understanding of unity of knowledge in social systems. Evolutionary equilibrium implications of such learning are formalized by means of the method of sets and topology.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".