Bibliographic record
Abstract
Over the years, the US has intervened covertly in many countries to remove dictators, subvert elected leaders, and support coups. Explanations for this focus on characteristics of target countries or strategic incentives to pursue regime change. This Element provides an account of domestic political factors constraining US presidents' authorization of covert foreign-imposed regime change operations (FIRCs), arguing that congressional attention to covert action alters the Executive's calculus by increasing the political costs associated with this secretive policy instrument. It shows that congressional attention is the result of institutional battles over abuses of executive authority and has a significant constraining effect independent of codified rules and partisan disputes. These propositions are tested using content analysis of the Congressional Record, statistical analysis of Cold War covert FIRCs, and causal-process evidence relating to covert interventions in Chile, Angola, Central America, Afghanistan, etc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.009 |
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".