Bibliographic record
Abstract
Improvements at local to global levels needs holistic understanding of the processes and actors (operators). The theory of technical systems provides a model for a holistic understanding, and a conceptual basis for qualitative thinking. The theory describes a transfo-rmation system, in an environment. Any artificial change (transformation) can be modeled in this way. The environment includes local influences and effects acting on the system and its process, and a general environment (regional, national and global) that covers physical, chemical, societal, economic, cultural, political, ideological, geographic and all other influences, with a link to other areas of study. Technical systems (as main operator) experience a typical life cycle. This leads to consideration of supply networks, globalization, financing, impacts on the environment, and other concepts. The process, and all operators, exhibit typical classes of properties. Each process and operator consists of elements and relationships that form structures of several useful kinds. Anticipating a future involves establishing the requirements, including those that arise from the producing organization. The available technological and scientific information influences the development of individual sorts of transformation system, and enables and limits the changes in culture that can be implemented. A rational methodology for designing newer transformation systems can be proposed. This systematic approach to designing allows use of other design methods, including intuitive working.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".