Policy Instruments and External Shocks: Explaining Differences in the Speed of U.S. Client State Acquisitions
Bibliographic record
Abstract
Explaining why political events occur at different speeds is usually done by invoking tipping points or shocks. We argue that the latter, although a starting point, is inadequate and has to be supplemented, at least for activities involving multiple bureaucracies, by the policy instruments embedded in organizations and capable of being deployed in particular times and places. This is illustrated by a focus on U.S. foreign policy, the aim being to account for why the United States took much longer to acquire client states in Central America and the Caribbean in the first two decades of the twentieth century than it did to acquire South American and Canadian clients at the outbreak of World War II. The argument is that shocks can deploy certain policy instruments faster and on a more wholesale basis than others. One of the striking features of political life is the great difference in the speed at which apparently similar events occur. We are all familiar with legislative struggles, organizing efforts, or military campaigns which advance at a snail’s pace for years or decades, only to conclude with rapid success. Typically, this sort of belated acceleration is explained by appeals to either tipping points, in which some incremental process finally crosses a threshold; or shocks, in which an
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".