Bibliographic record
Abstract
Abstract Concerns over disinformation have intensified in recent years. Policymakers, pundits, and observers worry that countries like Russia are spreading false narratives and disseminating rumours in order to shape international opinion and, by extension, government policies to their liking. Despite the importance of this topic, mainstream theories in International Relations offer contradictory guidance on how to think about disinformation. I argue that disinformation is ineffective in terms of changing the policies of a target as regards to its foreign policy alignments and armaments – that is, the balance of power. To be strategically effective, disinformation must somehow overcome three powerful obstacles: first, the fundamental uncertainty that international anarchy generates over any information broadcasted by adversaries; second, the pre-existing prejudices of foreign policy elites and ordinary citizens; and third, the countermeasures that are available even amid political polarisation. I examine the most likely case of there seemingly being a conscious and effective strategy that emphasises disinformation: the Russian campaign that has targeted the Baltic states, especially since the 2014 annexation of Crimea. The available evidence strongly suggests that the strategic effects of disinformation are exaggerated.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.004 | 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".