American Political Scientists on the Use of Force: Classifying the Concepts
Bibliographic record
Abstract
This article argues that existing general typologies of the use of force concepts accepted by American political scientists do not correspond with the reality. The survey compares several distinctive approaches which are generally proposed to classify the ideas elaborated in American political circuits and comes to the conclusion that none of the mentioned approaches could be applied directly to the use of force issue due to numerous difficulties occur while drawing on existing classifications. The article proposes a new method for systematizing these American political theories which is based on two main criteria: scholars’ attitude towards actual use of force and their perceptions of threats/challenges to national security. According to the newly introduced typology there are three major trends in American political thought on the issue: the first one consolidates those who support active and aggressive use of force (proponents of intervention or Interventionalists), the second one includes concepts of those authors who are not against the use of American forces abroad but stand on less aggressive positions (Conceptualists), and the third one unites those American political authors who insist that the US should use the force only in case of direct imminent attack on the American soil (Defenders).
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".