Bibliographic record
Abstract
In the first section of this article I describe the United States’ (U.S.) war on Iraq in March 2003 by way of a focus on the targeting of Baghdad and the U.S. military’s utilization of high-tech weapons of intelligence and targeting, including of Unmanned Aerial Vehicles (UAVs) and Special Operations Forces. In the second section, I argue that the new U.S. civilian-military elite associated with the Project for the New American Century (PNAC) is today waging a similar war inside the U.S. on the Utopia (society without war) of the global peace movement. Targeting what I call the Imaginist City, the new U.S. civilianmilitary elite is exploiting high-tech weapons of intelligence and targeting, UAVs and Special Operations Units with the aim of pursuing what I portray as the politics of noise. I illustrate my contentions in the third section by demonstrating how the new U.S. civilian-military elite and PNAC has activated a sequence of assaults on the Utopian society and Imaginist City of the global peace movement and how, following the domestic deployment of hightech weapons of intelligence and targeting such as UAVs on the U.S.-Mexican border and Special Operations Units in New York, the global peace movement might resist such attacks.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".