Our Man in Havana: Explaining the Causes, Conduct, and Consequences of Foreign Electoral Intervention
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
This dissertation is about foreign electoral intervention (FEI). Why do national-level politicians in democracies break laws and democratic norms by cooperating with foreign powers to win elections? Why do intervening states use aggressive methods of interference in some cases and light methods of influence in others? And does electoral intervention work? If a politician or a party owes some of its electoral victory to a foreign power, do they cooperate with that foreign power, once in government? To answer these questions, this dissertation uses original archive research to expand an existing dataset on FEIs, known as the ‘Partisan Electoral Interventions by the Great Powers’ (PEIG), and uses a Qualitative Comparative Analysis (QCA) to assess the cooperative outcomes of FEI. These findings were then compared against two case studies of American interventions in Canada in the 1960s and in El Salvador in the 1980s. My results show that political polarization predicts FEI, for both affective polarization and platform polarization. Strong democratic institutions do not prevent FEI, but they do prevent the most aggressive methods of FEI. In terms of consequences, FEI can produce a cooperative partner, but this is conditioned on the newly elected government being able to overcome domestic veto players in the legislature. Occasionally this requires the intervener to intervene a second time, to manipulate those veto players. The dissertation concludes by discussing the policy implications of these findings.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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