Bibliographic record
Abstract
The compliance section of Australia’s Department of Immigration enforces the departure of 10,000 people yearly. By international standards this is a very high number relative to population. At 5.5 per 10,000 head of population, Australia’s deportations rate is well ahead of the United Kingdom at 2.6 and Canada at 2.1. Aggressive deportation action is part of the Coalition government’s determination to be strong on border security, crime and terrorism. It gives no quarter to asylum seekers arriving without authorisation or non-citizens who become the subject of suspicion on national security grounds or are deemed to be of bad character. During the current term of the Coalition government we have seen the scandalous cases of Cornelia Rau, Vivian Alvarez, Robert Jovicic, and Mohammed Haneef. The Department of Immigration has promised a ‘cultural change’ and is spending $550 million over four years on fixing IT weaknesses. But the fundamental problem is that the deportation system itself is out of control. Australian officials must stop deporting people who are long term Australian residents; who are subjected to unsubstantiated suspicion; or who will be exposed to human rights abuses if deported. This requires changes to the Migration Act and its application led by the Immigration Minister. [Introduction]
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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; both teacher heads agree on what is shown here.
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".