Bibliographic record
Abstract
This paper analyses the Canadian government’s foreign and security policy responses to Russian disinformation in the context of the Russo-Ukrainian war. It asks whether, how, and why the government has securitised the “crisis of Russian disinformation.” The paper first briefly reviews literature on the Copenhagen’s School’s “securitisation” theory and how it has been used to explain responses to other crises. It then adopts the framework to contextualise the Canadian federal government’s official rhetoric, and then to categorise government policies and actions. The sources consulted include government actors’ reports and stated intentions and policies from 2022 to 2024. Adopting a securitisation framework reveals that Russian disinformation has been rhetorically securitised by government actors as an existential threat to national security and democratic integrity which requires urgent action. Within a context of cascading risks, the government has taken a range of distinct yet reinforcing policies and actions, some more comprehensive than others. The paper argues that together this “pervasive rhetorical securitisation” and “ad hoc practical securitisation” comprise the Canadian government’s ongoing process of partial securitisation. This process is legitimising different methods of governance: security and warfare communications (to address threats to national defence and security), democratic resilience (to address threats to democracy), and, most controversially, blocking and sanctioning (to signal discontent to the Russian regime). The analysis further reveals that each approach has different benefits and limits. The paper concludes that the securitisation process is incomplete compared to the government's rhetoric, with no over-arching organisation or strategy. It outlines implications for future research.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.030 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".