Bibliographic record
Abstract
This article examines the influence and interpretation of international law in Australia’s policy and conduct regarding captured individuals during the recent Afghanistan Conflict. By critically analysing declassified government documents, Parliamentary statements, and original interview data with former Foreign Minister and Defence Minister Stephen Smith, I advance a two-pronged argument. First, contrary to what other sombre studies of the anti-torture norm might predict, Australia’s understanding of fundamental international legal rules pertaining to captured individuals in armed conflict – including the humane treatment principle and the prohibition on torture – helped regulate its policies and actions during the Afghan war. By regulate, the article posits that Australia’s policies and behaviour were governed or controlled in part by a felt sense of legal obligation among some key policy-makers. Second, like its allies Britain and Canada, Australia claimed it did not formally detain individuals during the initial years of the Afghanistan Conflict, even though it appears to have factually captured and transferred some people to United States (us) and Afghan authorities. As the war dragged on, and Australia’s troop contributions increased and local hostilities worsened, Australia – again like its allies – relied on detainee agreements and changed its conduct to try to protect captured individuals and transferees from abuse. Despite such agreements and changes, critics contend that transferred captives faced a significant risk of torture in Afghan jails, particularly those run by the country’s intelligence agency. This suggests that state and non-state views of what the prohibition on transferring to possible torture requires in practice are less settled than related shared understandings of other fundamental prisoner protections in international law and armed conflict.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.071 | 0.017 |
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".