“Epidemic of Disinformation” Around Sars-Cov-2: Are Russian Counteractions Effective Against COVID-19?
Bibliographic record
Abstract
Accusations concerning the handling of the COVID-19 pandemic are mounting between Western and Eastern countries, where Russia is far from being an exception. Division lines exist even within blocs of countries having very similar political, socio-economic and cultural identities. There are as many ways to handle the COVID-19 pandemic as governments, international organisations etc., but the necessity of at least some forms of lockdown is almost universally agreed. Unfortunately, instead of abandoning previous conflicts and maximising international cooperation to successfully contain the pandemic, minimising casualties and social stress, virtually all aspects of the pandemic become over-politicised and used to advance competing interests. Russia is an important part of the “blame-game”. Having a culture of strong presidential power, harsh measures had been introduced meeting compliance of most of the society. As of November 2020, the number of newly infected people was still rising quite rapidly and systematically. Thus, the effectiveness of Russian countermeasures to tackle the pandemic was debated not only in Russia, but around the world.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".