Bibliographic record
Abstract
Subject to severe financial constraints while operating within a regime of moral panics driven by the `war on terrorism', higher education in the United States faces both a legitimation crisis and a political crisis. With its increasing reliance on Pentagon and corporate interests, the academy has largely opened its doors to serving private and governmental interests and in doing so has compromised its role as a democratic public sphere. This article situates the development of the university as a militarized knowledge factory within the broader context of what I call the biopolitics of militarization and its increasing influence and power within American society after the tragic events of September 11, 2001. Highlighting and critically engaging the specific ways in which the forces of militarization are shaping various aspects of university life, this article focuses on the growth of militarized knowledge and research, the increasing development of academic programs and schools that serve military personnel, and the ongoing production of military values and subject positions on US campuses. It also charts how the alliance between the university and the national security state has undermined the university as a site of criticism, dissent and critical dialogue.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".