Bibliographic record
Abstract
Paralleling Zakaria&s;s description of the varied military procession that marked Queen Victoria&s;s Diamond Jubilee, one can describe the make-up of a similar American parade today. It would consist of close protection specialists from South Africa, sentries from Chile, embassy guards from Australia, cleaners from the Philippines, drivers from India, and cooks from Nepal. All of these individuals would be contractors. During the last two decades in which Zakaria contends that “the United States was utterly unrivaled” and that this was “a phenomenon unprecedented in history,” the symbiotic relationship between the United States and the private security industry writ large grew to unrivaled levels. In Iraq, the ratio of contractors to troops was as high as 1:1, and in Afghanistan, at the time of writing, the figure was 1.6:1. 1 Just as the United States is now essential in shaping “the [private security] market&s;s ecology,” contractors, both armed and unarmed, are a key ingredient of American power projection: “[C] ontractors are not replacing force structure, they are becoming force structure.” 2 Given this intimate relationship, how might private security contribute to the bringing about of a post-American world? What might private security look like in a post-American world?
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".