Tightly Bound: The United States and Australia's Alliance-Dependent Militarization
Bibliographic record
Abstract
Contemporary Australia is a case of dependent, high-technology liberal militarization, but with distinctive characteristics pointing to a model that must look beyond standard concerns with increasing national defense budgets, more and better weapons systems, an “exceptionalist” approach to immigration security and a predilection for use of military force in international affairs. In a world and time where militarization is a global norm embedded in globe-spanning military alliances and world-wide networks of foreign military bases, discerning the lineaments of one particular national instance can be both difficult and potentially misleading. In liberal democracies, national self-conceptions resist identification with the harsh implications of reliance on, or valorization of, military force, unless it can be viably represented as defense of freedom, just war, or wars against unspeakable Others. And in the case of liberal democracies originating in a settler state with ongoing unrecognized conquest of indigenous peoples – think Australia, the United States, Canada and Israel – the racially inflected violence at the foundations of state-formation and national identity continues to ramify through the default settings of contemporary foreign policy. All three qualities distinguish the contemporary pattern of Australian militarization from the standard versions of either exceptionalist or liberal militarization.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".