Bibliographic record
Abstract
In response to the conflict between Ukraine and Russia, which has so far led to the Crimea being annexed by Russia, the USA and EU have introducted sanctions against individuals by way of refusing their entry and blocking their bank accounts and are threatening to increase the circle of those affected and to impose tight economic sanctions. The promised funding by the IMF to the tune of $ 14 to 18 billion for Ukraine is tied to conditions. Russia will no longer grant any discounts for natural gas deliveries. The EU countries agreeing on a uniform mechanism to wind up insolvent banks is another step towards completing the banking union. The Council cleared the way for adopting the European Directive on Taxation of Savings Income. Starting in 2015, interest payments received by non-nationals will be automatically reported to the internal revenue service at the non-national's fiscal domicile. – In Austria, tax increases and changes in tax concessions are being implemented in the 2014 Act Amending Taxes and Charges. After extended negotiations, the federal government decides to wind up insolvent Hypo Alpe-Adria-Bank International AG in a bad bank.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.346 | 0.371 |
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".