Deterrence and Foreign Election Intervention: Securing Democracy through Punishment, Denial, and Delegitimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Democratic elections are under threat from foreign interference. Democracies around the world are experimenting with a range of responses, from threatening potential interveners with a variety of retaliatory punishments to bolstering election security, scrutiny, and counter-interference operations, and leveraging multilateral institutions to reinforce norms of behavior and rules of engagement. At their root, many of these approaches are anchored to the logic of deterrence, compellence, and denial. Surprisingly, however, scholars and practitioners have paid scant attention to how contemporary approaches to countering foreign election intervention (FEI) fit within the larger framework of deterrence theory and practice. Our article reframes existing contemporary responses to FEI within the unifying context of fifth-wave deterrence scholarship, an emerging subtext of research on coercion with a penchant for all-domain observations, interdisciplinarity, and sub-threshold challenges that bridge the divide between national security and public safety. By exploring FEI within the context of contemporary deterrence, we sharpen our collective understanding of the phenomena and how best to respond. Our analysis encourages a more nuanced understanding of adversaries’ cost–benefit calculations in deciding whether, when and how to intervene in elections, and, crucially, helps identify the tools, technologies, infrastructures, and processes needed to manipulate an adversary's calculus and preferences. In doing so, we gain a better sense of the conditions under which states can deter FEI all the while securing national elections.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it