Deterrence and Disinformation: Communicating Deterrence in a Non-Linear Media Environment
Bibliographic record
Abstract
This paper investigates how the strategy of deterrence is relevant to understanding responses to disinformation in general, and in the case of Canada in 2014–23. First, it argues that extending a wide lens of deterrence to hybrid threats, including disinformation, highlights many options to deter by denial (mostly resilience) and by imposing widely defined ‘costs and punishments’, and reveals strengths and limits of both. Second, it shows that Canada’s efforts have intensified and shifted over time, resulting in a security and foreign policy approach focused on resilience to deny negative effects, and the imposition of costs and punishments to dissuade harmful actions. Third, it highlights benefits and limits of extending deterrence to disinformation in general and in the case of Canada. It suggests that deterrence principles and practices can further adapt to today’s non-linear information environment by engaging with the emerging academic field of strategic communications. In sum, the paper extends the literature on deterrence to disinformation, adds empirical knowledge about the evolution of the Canadian government’s efforts, and develops key critiques based on its findings. Ultimately, it suggests scholars conceptualise a ‘sixth wave of deterrence’ where the deterrence of complex challenges is communicated more strategically and long term within a contextualised, holistic, and ethically grounded approach.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".