Bibliographic record
Abstract
The growing intensity of world economic relations, and the expansion of the scale and volume of information flows between continents bring to the fore not only the problem of general cybersecurity but also the possibility/impossibility of producing new forms of media activity, which are based on both unconditional advantages and significant polystructural risks.Which are generated by new technological achievements of mankind.It follows from this that the modern media singularity of global economic relations should be understood as a large-scale process of self-reproduction of highly productive forms, methods, shells, the movement of variously oriented blocks of information prepared for perception, multi-level and sectoral replication, which provides competitive advantages to certain selective models and the rapid demise of others.This extraordinary characteristic of the media singularity, which is what we are talking about, was first identified by the American and, at the same time, Canadian scientist R.Florida [1], who saw in the formation of the latest economic model the so-called crisis of urbanism, which, according to the author, makes people unhappy.As partial support of this point of view, we note that a large city in the conditions of growing globalization is increasingly becoming a highly concentrated place of concentration -information hubs that carry out the export/import of ideologically motivated new knowledge, mostly
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".