The Western Alliance in the Face of the Russian (Dis)information Machine: Where Does Canada Stand?
Bibliographic record
Abstract
The Ukrainian crisis has witnessed intensification of information confrontation between Russia and the West. Canada - being an integral part of Western alliance and staunchly opposing to Russian actions on the Ukrainian Southeast – attracted Kremlin`s ire expressed in intensification of Russia`s information assault against it. The decision of Ottawa to deploy military forces in the Baltic Sea region and some legislative gestures were construed in Moscow as openly anti-Russian behaviour and a perfect example of Russophobia. This paper seeks to investigate the structure, key operative principles and distinctive features of Russia`s propaganda machine, and how these are used by the Russian side in its information campaign against Western alliance and Canada, in particular. The research demonstrates sophistication and elaborateness of Russia`s disinformation techniques: borrowing certain traits from the pre-1991 period, Russia managed to surpass its historical predecessor. This owed to the advent of new technologies and elimination of the ideological surcharge and previous dogmatism. On the basis of this research it could be argued that in spite of fierce disinformation assault that countries of the Western alliance have had to deal with after 2014, Russia`s resources are finite and Kremlin`s actions vary on a country-to-country basis. It thus could be stated that Russia`s capabilities against Canada in terms of information-phycological warfare are limited. This, however, does not mean that the peril is nonexistent. If (and, apparently, when) interests of Canada and Russia clash in the Arctic region, Moscow might be willing activate other means (the “cyber” pillar) of information confrontation, which have been tested in other theaters of Russia`s activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".