Bibliographic record
Abstract
The concept of a set is one of the key concepts in mathematics and not only. To further the discussion, assessments of the significance of sets in designing, generated by large language models, are given as a kind of generalized take on the importance of this concept. An analysis of the concepts of a set and a class in mathematics and engineering is provided. The multiplicity of their interpretations and the difference in various fields of application directly related to the concept of “element of the set”, are shown. In designing, the concept of a set is considered in various aspects and is filled with different content. First of all, it is a set of needs that continuously arise, and the design activity, accompanied by a multitude of design subjects participating in it, is aimed at satisfying them. It is also a set of precedents (already created artifacts), which are analogues and prototypes for the designer, but for one reason or another do not satisfy the emerging needs of the subjects. There are sets of: design parameters and design variables of the object being developed; criteria for evaluating a new artifact; background assumptions, data and conditions, including limitations; models describing the designed object; decision-making methods and, finally, a variety of design decisions. An objectively existing set of values of initial data and evaluation criteria in designing can be and is considered as an uncertainty (uncertain set), and as a given that must be revealed, narrowing the range of solutions and lowering the power of sets. In designing practice (removal of uncertainty, degeneration of the multiplicity of possible values of design parameters, etc.) one always strives for a singleton (a set with a single element), i.e. to the design solution that will be embodied in a design, technology or system.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.076 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".