The short road from the COVID-19 pandemic to the information warfare pandemic
Bibliographic record
Abstract
We have reached the level where we are already talking about an “infodemia” related to COVID-19. Not only the virus is spreading around the world, but also an increasing amount of information, real information warfare. The COVID-19 pandemic and the medical crisis that came with it are an opportunity for the world's major players already waging a wider information warfare, a campaign to show how well they manage the situation in their own states, and how they help globally. From here come the great failures, the great defeats and losses of those who tried to fish in troubled waters and to capitalize politically and as an image a crisis where there is extreme pain, many sick and where no one swallows this type of behavior, under any circumstances. In this respect, the present study represents a systemic and multidisciplinary research of the COVID-19 pandemic at different levels such as the political, economic, psychosocial, medical and information warfare one. Eventually, this research came up with an integrated strategic communication proposal which raises the attention regarding a more judicious involvement of the competent institutions of each EU country in the “information warfare” and, implicitly, in the related public communication.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".