8 субнормальных подгрупп классических интертипных отношений, которые могут породить 16 тетрахотомий социона: проблема взаимодействия подмножеств
Bibliographic record
Abstract
This is the twenty-second article in a series of publications devoted to exploring the mathematical properties of socionics. This article focuses on the eight tetrachotomies that can be obtained using left cosets generated from fourth-order subnormal classical intertype relation subgroups. It is shown that the use of left cosets, in contrast to right cosets, leads to tetrachotomies that have deterministic intertype relationships between each of its quarters. Four of these tetrachotomies can be defined with the Augusta-Reynin traits, and four can be defined with the Jung-Minaiev traits. In both cases, tetrachotomies are “two-formula”. “Formulas” are the three nontrivial classical intertype relation operators shared between the four types of each tetrachotomy quarter. For the four “two-formula” tetrachotomies that can be defined with the Augusta–Reynin traits, the quarters in the irrational half will have a different formula from the quarters in the rational half. For the four “two-formula” tetrachotomies that can be defined with the Jung–Minaiev traits, the quarters in the democratic half will have a different formula from the quarters in the aristocratic half.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".