Bibliographic record
Abstract
During the Obama administration years, the American military seemed to be withdrawing and American global hegemony withering. The administration had adopted a new foreign policy doctrine called “offshore balancing,” and its critics were many, dubbing the doctrine “neoisolationist.” However, this label has misdiagnosed the doctrine, which as this essay will first argue, can more accurately be labeled “pseudoneoisolationist”—the American military may have withdrawn its conventional forces to a degree, but it continues to become increasingly reliant on unmanned combat aerial vehicles (UCAVs). These UCAVs have allowed the United States to maintain Pax Americana while appearing to have staged a global retreat. This essay will then argue that two aspects of the American drone program have redefined territoriality: targeting methods and legal justification. This redefinition is a neoimperialist understanding, although admittedly it is one that cannot be correctly categorized as imperialist nor neoimperialist based on their traditional definitions. Finally, this essay will discuss why it is important to discuss the first two subjects (i.e. offshore balancing and the American drone program’s redefinition of territoriality) together, rather than in isolation.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".