Bibliographic record
Abstract
The development of civilization at the present stage faced with a phenomenon that is still poorly studied and little known, which we call trauma society. The fact is that meaningful, prominent and significant events are taking place in the world, which cannot be defined and qualified in the previous terms – evolution and revolution, which describe and reflect the current changes. At present, there are 53 States that, according to the world Bank, have been or are in a state of chaotic, unbalanced and turbulent development for a long period of time. Countries that are stagnating in their development for a long time or are in a state of recession and are losing previously achieved milestones are considered to be trauma societies. Special attention is paid to Russia, which, according to the author, can be attributed to traumatizes societies, since in its development, having rejected the socialist past, it did not reach the boundaries from which it began its journey. At the same time, the transformations that have been taking place for more than a quarter of a century form a mosaic in which it is difficult/impossible to distinguish between evolutionary and revolutionary trends. In this regard, an analysis of the obstacles that have not been overcome for the implementation of a truly democratic, effectively functioning society is given. The analysis of the state of trauma societies carried out in the scientific and expert community, based on the practice of successfully developing countries, allows us to determine ways out of the state of traumatized society.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".