Bibliographic record
Abstract
This chapter describes how Australia decided to launch a multilateral military intervention to stop the bloodshed in East Timor. The force Australia assembled was called the International Force East Timor (INTERFET). Despite the humanitarian character of the intervention, few of the participants joined INTERFET on their own initiative. Rather, Australia had to conduct an explicit recruitment process that involved cajoling countries to join the operation. Australia's diplomatic networks played an indispensable role in this process: Australian officials exploited these networks to retrieve information on deployment preferences of potential coalition participants. Australia also used the APEC summit in Auckland and the UN General Assembly (UNGA) in New York as opportunities to make bilateral appeals for troop contributions. Nevertheless, Australia's diplomatic cloud had its limitations. Especially when it came to recruiting countries from outside of the Asia-Pacific region, Australian networks were insufficient. Australia thus turned to the United States and the United Kingdom for assistance in drawing multilateral support for its coalition, thereby leaving these states to function as cooperation brokers. The chapter then considers the deployment decisions of the three largest troop-contributing countries: Thailand, Jordan, and the Philippines; Canada, a deeply embedded state with Australia; and Brazil, a weakly embedded state with Australia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".