Human Rights & The Australian Defence Force - Response to: How could Australia better protect and promote human rights and responsibilities?
Bibliographic record
Abstract
The ADF has often been literally at the sharp end of seeking to uphold human rights around the world, and it has also been accused of not meeting human rights standards at various times. The implications of enhanced human rights protections in Australia are potentially of profound significance to the ADF and its operations. The ADF is used to having to meet the standards of international humanitarian law (the law of armed conflict) and would adapt to any new human rights laws. The difference between international humanitarian law though and human rights law is that humanitarian law developed to cover military operations whereas with human rights law, military issues are usually an afterthought. As an academic who writes on the Australian Defence Force and the law, and as a reservist and former permanent naval officer, I would like to see the National Human Rights Consultation consider military issues from the outset. Discussions with academic and military colleagues have brought to my attention the experience of the UK, Canada and Germany. It illustrates that human rights laws developed for civilian society can have unexpected consequences for the military and its operations. Australia would do well to learn from these experiences so that any new enhanced human rights laws work in the unique context of the ADF and its operations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".