Bibliographic record
Abstract
When I first wrote the paper that formed the basis of this chapter, in August 2001, an Australian frigate just off Christmas Island was talcing on board over 450 Afghan and other refugees and immigrants from the Tampa, a Norwegian cargo ship which had rescued them from their sinking boat, for a voyage to New Guinea, and thence to Nauru and New Zealand. Polls showed that 77 per cent of Australians supported their government's refusal to allow the refugees to land on Australian soil. Broadsheet newspapers printed an enormous number of letters debating the pros and cons of the government's policy, while talkback radio carried many voices expressing hatred of the refugees and their supporters. It became clear from these exchanges that the desire to protect Australia's borders from refugees and those designated 'illegal immigrants' was and remains intimately entwined with questions of race, culture, immigration, refugee policy, the law of the sea, human rights, international covenants, and much else besides. As the Tampa lay in limbo off Christmas Island, American ABC television news likened its plight to the 'Voyage of the Damned', the ill-fated SS St Louis, a ship carrying Jewish refugees from Germany that in 1939 was not allowed to land in the United States or Canada and so returned with its passengers to Europe, where more than half later lost their lives in concentration camps.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".