Digital-era Propaganda: A Credible Threat to National and Global Security
Bibliographic record
Abstract
This paper seeks to define and analyze the difference between public policy and propaganda, the mechanisms of disseminating both disinformation and misinformation and the possible consequences, taking into account both technological and psychological factors. As regards the technological factors, particular attention is given to “deep fakes” that allow the creation of audio and video of real people saying and doing things they have never said or done, sheer invention. Machine learning techniques are accelerating technology’s sophistication, making deep fakes increasingly more realistic and resistant to detection. Psychological factors include the tendency of average people to fell prey to conspiracy theories and the confusion created by the plethora of “news sources” which are both easily available and unregulated. Strategies and tactics of digital-era propaganda will be examined based on contemporary case studies (including Russian campaigns to damage EU-Ukraine relations, and President Trump’s accusations against his adversaries for fake news). Recommendations will be given as how to counter the threat to the best possible extent. Moreover, focus is also placed on the danger of using the suppression of propaganda as a pretext to suppress media pluralism and control dissident voices that criticize the established status quo, especially in non-liberal democracies (like Russia) or in autocratic regimes (like China).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".