Ծայրահեղականությունը համաշխարհային տնտեսությունում. պատժամիջոցների մեջ «թաթախված» կյանք / Extremism in the World Economy: Life Deep in Sanctions
Bibliographic record
Abstract
Recently, sanctions have become a unique tool for regulating foreign relations and forcibly limiting the economic opportunities of other countries. In this regard, 2022 is an unprecedented year in terms of scope, coverage and scalability of sanctions. Although the practice of imposing sanctions is accompanied by “noise and shout” of humanism and pacifism, their main motivation is “political expediency”. The latter is justified by the use of double standards when imposing sanctions on a particular country that exhibits similar behavior. Based on the well-known Russian-Ukrainian events of 2022, the West “flooded” Russia with a stream of sanctions. As of November 9, the US, Canada, Switzerland, EU, UK, France, Australia and Japan have imposed a total of 12,747 sanctions against Russia. The sanctions packages also include a ban on imports from Russia of a number of vital resources that are, in fact, critical to the economy of the sanctioners themselves. In such circumstances, it is still difficult to clearly assess which of the parties will be more deeply "mired" in losses as a result of all this.
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 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".